survey

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Authors:

Yang XiangAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 55, Issue 1

Article No.: 22, Pages 1 - 51

https://doi.org/10.1145/3485273

Published: 23 November 2021 Publication History

Abstract

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

References

[1]

Mohamed Ahmed, Stella Spagna, Felipe Huici, and Saverio Niccolini. 2013. A peek into the future: Predicting the evolution of popularity in user generated content. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 607–616.

Digital Library

[2]

Abdulrahman Hamad E. Alarifi, Darshana Sedera, and Jan Recker. 2015. Posters versus lurkers: Improving participation in enterprise social networks through promotional messages. In Proceedings of the 36th International Conference on Information Systems (ICIS’15), J. Ross and D. Leidner (Eds.). Association for Information Systems. Retrieved from http://aisel.aisnet.org/.

[3]

Tim Althoff, Pranav Jindal, and Jure Leskovec. 2017. Online actions with offline impact: How online social networks influence online and offline user behavior. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM’17). Association for Computing Machinery, New York, NY, 537–546.

Digital Library

[4]

Sultan Alzahrani, Saud Alashri, Anvesh Reddy Koppela, Hasan Davulcu, and Ismail Hakki Toroslu. 2015. A network-based model for predicting hashtag breakouts in Twitter. In Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Washington, DC, USA, March 31–April 3, 2015. Proceedings(Lecture Notes in Computer Science, Vol. 9021), Nitin Agarwal, Kevin Xu, and Nathaniel Osgood (Eds.). Springer, 3–12.

[5]

John R. Anderson. 2007. How Can the Human Mind Occur in the Physical Universe? Oxford University Press, New York.

[6]

Adam Badawy, Emilio Ferrara, and Kristina Lerman. 2018. Analyzing the digital traces of political manipulation: The 2016 Russian interference Twitter campaign. In IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, August 28–31, 2018, Ulrik Brandes, Chandan Reddy, and Andrea Tagarelli (Eds.). IEEE Computer Society, 258–265.

Digital Library

[7]

Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. The role of social networks in information diffusion. In Proceedings of the 21st International Conference on World Wide Web (WWW’12). Association for Computing Machinery, New York, NY, 519–528.

Digital Library

[8]

Peng Bao. 2016. Modeling and predicting popularity dynamics via an influence-based self-excited Hawkes process. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM’16). ACM, New York, NY, 1897–1900.

Digital Library

[9]

Peng Bao, Hua-Wei Shen, Xiaolong Jin, and Xue-Qi Cheng. 2015. Modeling and predicting popularity dynamics of microblogs using self-excited Hawkes processes. In Proceedings of the 24th International Conference on World Wide Web. ACM, 9–10.

Digital Library

[10]

Nicola Barbieri, Francesco Bonchi, and Giuseppe Manco. 2013. Topic-aware social influence propagation models. Knowl. Inf. Syst. 37, 3 (2013), 555–584.

[11]

Livio Bioglio and Ruggero G. Pensa. 2017. Modeling the impact of privacy on information diffusion in social networks. In Complex Networks VIII, Bruno Gonçalves, Ronaldo Menezes, Roberta Sinatra, and Vinko Zlatic (Eds.). Springer International Publishing, Cham, 95–107.

[12]

Marián Boguá, Romualdo Pastor-Satorras, and Alessandro Vespignani. 2003. Epidemic Spreading in Complex Networks with Degree Correlations. Springer Berlin, 127–147.

[13]

Phillip Bonacich. 1987. Power and centrality: A family of measures. Amer. J. Sociol.ogy 92, 5 (1987), 1170–1182.

[14]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 2787–2795.

Digital Library

[15]

Simon Bourigault, Cedric Lagnier, Sylvain Lamprier, Ludovic Denoyer, and Patrick Gallinari. 2014. Learning social network embeddings for predicting information diffusion. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, 393–402.

Digital Library

[16]

Simon Bourigault, Sylvain Lamprier, and Patrick Gallinari. 2016. Representation learning for information diffusion through social networks: An embedded cascade model. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. ACM, 573–582.

Digital Library

[17]

Qi Cao, Huawei Shen, Keting Cen, Wentao Ouyang, and Xueqi Cheng. 2017. DeepHawkes: Bridging the gap between prediction and understanding of information cascades. In Proceedings of the ACM on Conference on Information and Knowledge Management. ACM, 1149–1158.

Digital Library

[18]

Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, and Xueqi Cheng. 2020. Popularity prediction on social platforms with coupled graph neural networks. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM’20). Association for Computing Machinery, New York, NY, 70–78.

Digital Library

[19]

Xuanyu Cao, Yan Chen, Chunxiao Jiang, and K. J. Ray Liu. 2016. Evolutionary information diffusion over heterogeneous social networks. IEEE Trans. Sig. Inf. Process. Netw. 2, 4 (2016), 595–610.

[20]

Michele Catanzaro, Marián Boguñá, and Romualdo Pastor-Satorras. 2005. Generation of uncorrelated random scale-free networks. Phys. Rev. E 71, 2 (Feb. 2005), 027103.

[21]

China Internet Network Information Center. 2019. The 43rd China Statistical Report on Internet Development(in Chinese). Retrieved February 1, 2019 from http:// www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201902/ P020190318523029756345.pdf/.

[22]

Wei Chen, Wei Lu, and Ning Zhang. 2012. Time-critical influence maximization in social networks with time-delayed diffusion process. In Proceedings of the 26th AAAI Conference on Artificial Intelligence.

Digital Library

[23]

Xueqin Chen, Kunpeng Zhang, Fan Zhou, Goce Trajcevski, Ting Zhong, and Fengli Zhang. 2019. Information cascades modeling via deep multi-task learning. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21–25, 2019, Benjamin Piwowarski, Max Chevalier, Éric Gaussier, Yoelle Maarek, Jian-Yun Nie, and Falk Scholer (Eds.). ACM, 885–888.

Digital Library

[24]

Xueqin Chen, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Fengli Zhang. 2019. Information diffusion prediction via recurrent cascades convolution. In Proceedings of the 35th IEEE International Conference on Data Engineering. IEEE, 770–781.

[25]

Justin Cheng, Lada Adamic, P. Alex Dow, Jon Michael Kleinberg, and Jure Leskovec. 2014. Can cascades be predicted? In Proceedings of the 23rd International Conference on World Wide Web. ACM, 925–936.

Digital Library

[26]

C. Chiang and J. Wang. 2015. The impact of interaction networks on lurkers’ behavior in online community. In Proceedings of the 48th Hawaii International Conference on System Sciences. 1645–1656.

Digital Library

[27]

Peter Clifford and Aidan Sudbury. 1973. A model for spatial conflict. Biometrika 60, 3 (1973), 581–588.

[28]

Josh Constine. 2017. Facebook Now Has 2 Billion Monthly Users. Retrieved June 28, 2017 from https://techcrunch.com/2017/06/27/facebook-2-billion-users/.

[29]

David Crandall, Dan Cosley, Daniel Huttenlocher, Jon Kleinberg, and Siddharth Suri. 2008. Feedback effects between similarity and social influence in online communities. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 160–168.

Digital Library

[30]

Riley Crane and Didier Sornette. 2008. Robust dynamic classes revealed by measuring the response function of a social system. Proc. Nat. Acad. Sci. United States Amer. 105, 41 (2008), 15649–15653.

[31]

Peng Cui, Shifei Jin, Linyun Yu, Fei Wang, Wenwu Zhu, and Shiqiang Yang. 2013. Cascading outbreak prediction in networks: A data-driven approach. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 901–909.

Digital Library

[32]

Peng Cui, Fei Wang, Shaowei Liu, Mingdong Ou, Shiqiang Yang, and Lifeng Sun. 2011. Who should share what?: Item-level social influence prediction for users and posts ranking. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 185–194.

Digital Library

[33]

D. J. Daley and D. G. Kendall. 1964. Epidemics and rumours. Nature 204, 4963 (1964), 1118–1118.

[34]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5–10, 2016, Barcelona, Spain, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 3837–3845.

Digital Library

[35]

Sahraoui Dhelim, Nyothiri Aung, Mohammed Amine Bouras, Huansheng Ning, and Erik Cambria. 2021. A survey on personality-aware recommendation systems. arXiv preprint arXiv:2101.12153 (2021).

[36]

Odo Diekmann and Johan Andre Peter Heesterbeek. 2000. Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation. Vol. 5. John Wiley & Sons.

[37]

Jingtao Ding, Yanghao Li, Yong Li, and Depeng Jin. 2018. Click versus share: A feature-driven study of micro-video popularity and virality in social media. In Proceedings of the 2018 SIAM International Conference on Data Mining, SDM 2018, May 3–5, 2018, San Diego Marriott Mission Valley, San Diego, CA, USA, Martin Ester and Dino Pedreschi (Eds.). SIAM, 198–206.

[38]

Keyan Ding, Ronggang Wang, and Shiqi Wang. 2019. Social media popularity prediction: A multiple feature fusion approach with deep neural networks. In Proceedings of the 27th ACM International Conference on Multimedia, MM 2019, Nice, France, October 21–25, 2019, Laurent Amsaleg, Benoit Huet, Martha A. Larson, Guillaume Gravier, Hayley Hung, Chong-Wah Ngo, and Wei Tsang Ooi (Eds.). ACM, 2682–2686.

Digital Library

[39]

Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song. 2016. Recurrent marked temporal point processes: Embedding event history to vector. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). Association for Computing Machinery, New York, NY, 1555–1564.

Digital Library

[40]

Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, and Le Song. 2015. Coevolve: A joint point process model for information diffusion and network co-evolution. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 1954–1962.

Digital Library

[41]

Emilio Ferrara and Zeyao Yang. 2015. Quantifying the effect of sentiment on information diffusion in social media. PeerJ Comput. Sci. 1 (2015), e26.

[42]

Flavio Figueiredo, Jussara M. Almeida, Marcos A. Gonçalves, and Fabricio Benevenuto. 2016. Trendlearner: Early prediction of popularity trends of user generated content. Inf. Sci. 349 (2016), 172–187.

Digital Library

[43]

Noah Friedkin and Eugene Johnsen. 1999. Social influence networks and opinion change. Adv. Group Process. 16 (01 1999), 1–29.

[44]

Minglei Fu, Jun Feng, Dmytro Lande, Oleh Dmytrenko, Dmytro Manko, and Ryhor Prakapovich. 2021. Dynamic model with super spreaders and lurker users for preferential information propagation analysis. Phys. A: Statist. Mech. Applic. 561 (2021), 125266.

[45]

Shuai Gao, Jun Ma, and Zhumin Chen. 2014. Effective and effortless features for popularity prediction in microblogging network. In Proceedings of the 23rd International Conference on World Wide Web. ACM, 269–270.

Digital Library

[46]

Shuai Gao, Jun Ma, and Zhumin Chen. 2015. Modeling and predicting retweeting dynamics on microblogging platforms. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM’15). ACM, New York, NY, 107–116.

Digital Library

[47]

Sheng Gao, Huacan Pang, Patrick Gallinari, Jun Guo, and Nei Kato. 2017. A novel embedding method for information diffusion prediction in social network big data. IEEE Trans. Industr. Inform. 13, 4 (2017), 2097–2105.

[48]

Xiaofeng Gao, Zhenhao Cao, Sha Li, Bin Yao, Guihai Chen, and Shaojie Tang. 2019. Taxonomy and evaluation for microblog popularity prediction. ACM Trans. Knowl. Discov. Data 13, 2 (2019), 15.

Digital Library

[49]

Javad Ghaderi and R. Srikant. 2014. Opinion dynamics in social networks with stubborn agents: Equilibrium and convergence rate. Automatica 50, 12 (2014), 3209–3215.

Digital Library

[50]

George Giakkoupis, Rachid Guerraoui, Arnaud Jégou, Anne-Marie Kermarrec, and Nupur Mittal. 2015. Privacy-conscious information diffusion in social networks. In DISC 2015(29th International Symposium on Distributed Computing, Vol. LNCS 9363), Yoram Moses and Matthieu Roy (Eds.). Toshimitsu Masuzawa and Koichi Wada, Springer-Verlag Berlin, Tokyo, Japan.

Digital Library

[51]

Jacob Goldenberg, Barak Libai, and Eitan Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Market. Lett. 12, 3 (2001), 211–223.

[52]

Manuel Gomez-Rodriguez, David Balduzzi, and Bernhard Schölkopf. 2011. Uncovering the temporal dynamics of diffusion networks. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011, Lise Getoor and Tobias Scheffer (Eds.). Omnipress, 561–568.

Digital Library

[53]

Manuel Gomez-Rodriguez, Jure Leskovec, and Andreas Krause. 2012. Inferring networks of diffusion and influence. ACM Trans. Knowl. Discov. Data 5, 4 (2012), 21.

Digital Library

[54]

Manuel Gomez Rodriguez, Jure Leskovec, and Bernhard Schölkopf. 2013. Structure and dynamics of information pathways in online media. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 23–32.

Digital Library

[55]

Manuel Gomez-Rodriguez and Bernhard Schölkopf. 2012. Submodular inference of diffusion networks from multiple trees. In Proceedings of the 29th International Coference on International Conference on Machine Learning. 1587–1594.

Digital Library

[56]

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8–13 2014, Montreal, Quebec, Canada, Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 2672–2680.

Digital Library

[57]

Chengcheng Gou, Huawei Shen, Pan Du, Dayong Wu, Yue Liu, and Xueqi Cheng. 2018. Learning sequential features for cascade outbreak prediction. Knowl. Inf. Syst. 57, 3 (2018), 721–739.

Digital Library

[58]

Mark Granovetter. 1978. Threshold models of collective behavior. Amer. J. Sociol. 83, 6 (1978), 1420–1443.

[59]

Daniel Gruhl, Ramanathan Guha, David Liben-Nowell, and Andrew Tomkins. 2004. Information diffusion through blogspace. In Proceedings of the 13th International Conference on World Wide Web. ACM, 491–501.

Digital Library

[60]

Adrien Guille and Hakim Hacid. 2012. A predictive model for the temporal dynamics of information diffusion in online social networks. In Proceedings of the 21st International Conference on World Wide Web. ACM, 1145–1152.

Digital Library

[61]

Adrien Guille, Hakim Hacid, Cecile Favre, and Djamel A. Zighed. 2013. Information diffusion in online social networks: A survey. ACM SIGMOD Rec. 42, 2 (2013), 17–28.

Digital Library

[62]

Per Hage and Frank Harary. 1995. Eccentricity and centrality in networks. Soc. Netw. 17, 1 (1995), 57–63.

[63]

Keqi Han, Yuan Tian, Yunjia Zhang, Ling Han, Hao Huang, and Yunjun Gao. 2020. Statistical estimation of diffusion network topologies. In Proceedings of the 36th IEEE International Conference on Data Engineering. IEEE, 625–636.

[64]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 770–778.

[65]

Xinran He, Guojie Song, Wei Chen, and Qingye Jiang. 2012. Influence blocking maximization in social networks under the competitive linear threshold model. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 463–474.

[66]

Zaobo He, Zhipeng Cai, Jiguo Yu, Xiaoming Wang, Yunchuan Sun, and Yingshu Li. 2016. Cost-efficient strategies for restraining rumor spreading in mobile social networks. IEEE Trans. Vehic. Technol. 66, 3 (2016), 2789–2800.

[67]

Minh X. Hoang, Xuan-Hong Dang, Xiang Wu, Zhenyu Yan, and Ambuj K. Singh. 2017. GPOP: Scalable group-level popularity prediction for online content in social networks. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 725–733.

Digital Library

[68]

Tuan-Anh Hoang and Ee-Peng Lim. 2016. Microblogging content propagation modeling using topic-specific behavioral factors. IEEE Trans. Knowl. Data Eng. 28, 9 (2016), 2407–2422.

Digital Library

[69]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.

Digital Library

[70]

Liangjie Hong, Ovidiu Dan, and Brian D. Davison. 2011. Predicting popular messages in Twitter. In Proceedings of the 20th International Conference Companion on World Wide Web. ACM, 57–58.

Digital Library

[71]

Hongxin Hu, Gail-Joon Ahn, and Jan Jorgensen. 2011. Detecting and resolving privacy conflicts for collaborative data sharing in online social networks. In Twenty-Seventh Annual Computer Security Applications Conference, ACSAC 2011, Orlando, FL, USA, 5–9 December 2011, Robert H’obbes’ Zakon, John P. McDermott, and Michael E. Locasto (Eds.). ACM, 103–112.

Digital Library

[72]

Liang’an Huo and Chenyang Ma. 2017. Dynamical analysis of rumor spreading model with impulse vaccination and time delay. Phys. A: Statist. Mech. Applic. 471 (2017), 653–665.

[73]

Roberto Interdonato, Chiara Pulice, and Andrea Tagarelli. 2015. “Got to Have Faith!”: The DEvOTION algorithm for delurking in social networks. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (ASONAM’15). Association for Computing Machinery, New York, NY, 314–319.

Digital Library

[74]

R. Interdonato, C. Pulice, and A. Tagarelli. 2016. Community-based delurking in social networks. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’16). 263–270.

Digital Library

[75]

José Luis Iribarren and Esteban Moro. 2009. Impact of human activity patterns on the dynamics of information diffusion. Phys. Rev. Lett. 103, 3 (July 2009), 038702.

[76]

Mohammad Raihanul Islam, Sathappan Muthiah, Bijaya Adhikari, B. Aditya Prakash, and Naren Ramakrishnan. 2018. DeepDiffuse: Predicting the “Who” and “When” in cascades. In Proceedings of the IEEE International Conference on Data Mining. IEEE Computer Society, 1055–1060.

[77]

Tomoharu Iwata, Amar Shah, and Zoubin Ghahramani. 2013. Discovering latent influence in online social activities via shared cascade Poisson processes. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). ACM, New York, NY, 266–274.

Digital Library

[78]

Bo Jiang, Jiguang Liang, Ying Sha, and Lihong Wang. 2015. Message clustering based matrix factorization model for retweeting behavior prediction. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management. ACM, 1843–1846.

Digital Library

[79]

Chunxiao Jiang, Yan Chen, and K. J. Ray Liu. 2014. Evolutionary dynamics of information diffusion over social networks. IEEE Trans. Sig. Process.ing 62, 17 (2014), 4573–4586.

[80]

Chunxiao Jiang, Yan Chen, and K. J. Ray Liu. 2014. Graphical evolutionary game for information diffusion over social networks. IEEE J. Select. Topics Sig. Process. 8, 4 (2014), 524–536.

[81]

Jiaojiao Jiang, Sheng Wen, Shui Yu, Yang Xiang, and Wanlei Zhou. 2016. Rumor source identification in social networks with time-varying topology. IEEE Trans. Depend. Secure Comput. 15, 1 (2016), 166–179.

Digital Library

[82]

Jiaojiao Jiang, Sheng Wen, Shui Yu, Yang Xiang, and Wanlei Zhou. 2017. Identifying propagation sources in networks: State-of-the-art and comparative studies. IEEE Commun. Surv. Tutor. 19, 1 (2017), 465–481.

Digital Library

[83]

Meng Jiang, Peng Cui, Rui Liu, Qiang Yang, Fei Wang, Wenwu Zhu, and Shiqiang Yang. 2012. Social contextual recommendation. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM, 45–54.

Digital Library

[84]

Meng Jiang, Peng Cui, Fei Wang, Xinran Xu, Wenwu Zhu, and Shiqiang Yang. 2014. FEMA: Flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1186–1195.

Digital Library

[85]

Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, and Shiqiang Yang. 2016. Little is much: Bridging cross-platform behaviors through overlapped crowds. In Proceedings of the 30th AAAI Conference on Artificial Intelligence.

Digital Library

[86]

Renlong Jie, Jian Qiao, Genjiu Xu, and Yingying Meng. 2016. A study on the interaction between two rumors in homogeneous complex networks under symmetric conditions. Phys. A: Statist. Mech. Applic. 454 (2016), 129–142.

[87]

David Kempe, Jon Kleinberg, and Éva Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 137–146.

Digital Library

[88]

David Kempe, Jon Kleinberg, and Éva Tardos. 2005. Influential nodes in a diffusion model for social networks. In Proceedings of the International Colloquium on Automata, Languages, and Programming. Springer, 1127–1138.

Digital Library

[89]

Jo Ling Kent. 2011. Chinese Scramble to Buy Salt as Radiation Fears Grow. Retrieved March 18, 2011 from http://www.cnn.com/2011/WORLD/asiapcf/03/17/china.salt.scramble /index.html/.

[90]

William Ogilvy Kermack and Anderson G. McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proc. Roy. Societ. Lond. Series A, Contain. Papers Math. Phys. Char. 115, 772 (1927), 700–721.

[91]

Habibul Haque Khondker. 2011. Role of the new media in the Arab spring. Globalizations 8, 5 (2011), 675–679.

[92]

Jae Kyeong Kim, Hyea Kyeong Kim, Hee Young Oh, and Young U. Ryu. 2010. A group recommendation system for online communities. Int. J. Inf. Manag. 30, 3 (2010), 212–219.

Digital Library

[93]

Masahiro Kimura, Kazumi Saito, Kouzou Ohara, and Hiroshi Motoda. 2012. Opinion formation by voter model with temporal decay dynamics. In Machine Learning and Knowledge Discovery in Databases, Peter A. Flach, Tijl De Bie, and Nello Cristianini (Eds.). Springer Berlin, 565–580.

Digital Library

[94]

Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14–16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.).

[95]

Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations.

[96]

David G. Kleinbaum and Mitchel Klein. 2010. Survival Analysis. Vol. 3. Springer.

[97]

Ryota Kobayashi and Renaud Lambiotte. 2016. Tideh: Time-dependent Hawkes process for predicting retweet dynamics. In Proceedings of the 10th International AAAI Conference on Web and Social Media.

[98]

Tamara G. Kolda, Ali Pinar et al. 2014. FEASTPACK v1. 2. Sandia National Laboratories, Albuquerque, NM. Retrieved from https://old-www.sandia.gov/tgkolda/feastpack/.

[99]

Tamara G. Kolda, Ali Pinar, Todd Plantenga, and C. Seshadhri. 2014. A scalable generative graph model with community structure. SIAM J. Sci. Comput. 36, 5 (2014), C424–C452. arXiv:https://doi.org/10.1137/130914218.

[100]

Quyu Kong. 2019. Linking epidemic models and Hawkes point processes for modeling information diffusion. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM’19). Association for Computing Machinery, New York, NY, 818–819.

[101]

Shoubin Kong, Qiaozhu Mei, Ling Feng, Fei Ye, and Zhe Zhao. 2014. Predicting bursts and popularity of hashtags in real-time. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 927–930.

Digital Library

[102]

Andrey Kupavskii, Liudmila Ostroumova, Alexey Umnov, Svyatoslav Usachev, Pavel Serdyukov, Gleb Gusev, and Andrey Kustarev. 2012. Prediction of retweet cascade size over time. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12). Association for Computing Machinery, New York, NY, 2335–2338.

Digital Library

[103]

Kristina Lerman and Rumi Ghosh. 2010. Information contagion: An empirical study of the spread of news on Digg and Twitter social networks. In Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, USA, May 23–26, 2010, William W. Cohen and Samuel Gosling (Eds.). The AAAI Press. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1509.

[104]

Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, and Zoubin Ghahramani. 2010. Kronecker graphs: An approach to modeling networks. J. Mach. Learn. Res. 11 (Mar. 2010), 985–1042.

Digital Library

[105]

Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2007. Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1, 1 (Mar. 2007), 2–es.

Digital Library

[106]

Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. Retrieved from http://snap.stanford.edu/data.

[107]

Cheng Li, Jiaqi Ma, Xiaoxiao Guo, and Qiaozhu Mei. 2017. DeepCas: An end-to-end predictor of information cascades. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). International World Wide Web Conferences Steering Committee, 577–586.

Digital Library

[108]

Dong Li and Jiming Liu. 2021. Modeling influence diffusion over signed social networks. IEEE Trans. Knowl. Data Eng. 33, 2 (2021), 613–625.

[109]

Dong Li, Shengping Zhang, Xin Sun, Huiyu Zhou, Sheng Li, and Xuelong Li. 2017. Modeling information diffusion over social networks for temporal dynamic prediction. IEEE Trans. Knowl. Data Eng. 29, 9 (2017), 1985–1997.

Digital Library

[110]

Dong Li, Yongchao Zhang, Zhiming Xu, Dianhui Chu, and Sheng Li. 2016. Exploiting information diffusion feature for link prediction in Sina Weibo. Sci. Rep. 6 (2016), 20058.

[111]

Mei Li, Xiang Wang, Kai Gao, and Shanshan Zhang. 2017. A survey on information diffusion in online social networks: Models and methods. Information 8, 4 (2017), 118.

[112]

Michael Y. Li and James S. Muldowney. 1995. Global stability for the SEIR model in epidemiology. Math. Biosci. 125, 2 (1995), 155–164.

[113]

Qian Li, Zheng Wang, Bin Wu, and Yunpeng Xiao. 2019. Competition and cooperation: Dynamical interplay diffusion between social topic multiple messages in multiplex networks. IEEE Trans. Comput. Soc. Syst. 6, 3 (2019), 467–478.

[114]

Weihua Li, Shaoting Tang, Wenyi Fang, Quantong Guo, Xiao Zhang, and Zhiming Zheng. 2015. How multiple social networks affect user awareness: The information diffusion process in multiplex networks. Phys. Rev. E 92, 4 (Oct. 2015), 042810.

[115]

Yanhua Li, Wei Chen, Yajun Wang, and Zhi-Li Zhang. 2015. Voter model on signed social networks. Internet Math. 11, 2 (2015), 93–133. arXiv:https://doi.org/10.1080/15427951.2013.862884.

[116]

Yuchen Li, Ju Fan, Yanhao Wang, and Kian-Lee Tan. 2018. Influence maximization on social graphs: A survey. IEEE Trans. Knowl. Data Eng. 30, 10 (2018), 1852–1872.

[117]

Dongliang Liao, Jin Xu, Gongfu Li, Weijie Huang, Weiqing Liu, and Jing Li. 2019. Popularity prediction on online articles with deep fusion of temporal process and content features. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press, 200–207.

Digital Library

[118]

Yanbing Liu, Jinzhe Zhao, and Yunpeng Xiao. 2018. C-RBFNN: A user retweet behavior prediction method for hotspot topics based on improved RBF neural network. Neurocomputing 275 (2018), 733–746.

Digital Library

[119]

Tiancheng Lou and Jie Tang. 2013. Mining structural hole spanners through information diffusion in social networks. In Proceedings of the 22nd International Conference on World Wide Web. ACM, 825–836.

Digital Library

[120]

Wei Lu, Wei Chen, and Laks V. S. Lakshmanan. 2015. From competition to complementarity: Comparative influence diffusion and maximization. Proc. VLDB Endow. 9, 2 (2015), 60–71.

Digital Library

[121]

Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. SoRec: Social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM Conference on Information and Knowledge Management. ACM, 931–940.

Digital Library

[122]

Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King. 2011. Recommender systems with social regularization. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 287–296.

Digital Library

[123]

Zongyang Ma, Aixin Sun, and Gao Cong. 2012. Will this# hashtag be popular tomorrow? In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1173–1174.

Digital Library

[124]

Zongyang Ma, Aixin Sun, and Gao Cong. 2013. On predicting the popularity of newly emerging hashtags in Twitter. J. Assoc. Inf. Sci. Technol. 64, 7 (2013), 1399–1410.

[125]

Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, and Christos Faloutsos. 2012. Rise and fall patterns of information diffusion: Model and implications. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 6–14.

Digital Library

[126]

Robert M. May and Alun L. Lloyd. 2001. Infection dynamics on scale-free networks. Phys. Rev. E 64, 6 (Nov. 2001), 066112.

[127]

Julian McAuley and Jure Leskovec. 2012. Learning to discover social circles in ego networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS’12). Curran Associates Inc., Red Hook, NY, 539–547.

Digital Library

[128]

Priyanka Meel and Dinesh Kumar Vishwakarma. 2020. Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities. Exp. Syst. Applic. 153 (2020), 112986.

[129]

Swapnil Mishra, Marian-Andrei Rizoiu, and Lexing Xie. 2016. Feature driven and point process approaches for popularity prediction. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management. ACM, 1069–1078.

Digital Library

[130]

Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. 2007. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. ACM, 29–42.

Digital Library

[131]

Seth Myers and Jure Leskovec. 2010. On the convexity of latent social network inference. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 1741–1749.

Digital Library

[132]

Seth A. Myers and Jure Leskovec. 2014. The bursty dynamics of the Twitter information network. In Proceedings of the 23rd International Conference on World Wide Web. ACM, 913–924.

Digital Library

[133]

Seth A. Myers, Chenguang Zhu, and Jure Leskovec. 2012. Information diffusion and external influence in networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 33–41.

Digital Library

[134]

Nasir Naveed, Thomas Gottron, Jérôme Kunegis, and Arifah Che Alhadi. 2011. Bad news travel fast: A content-based analysis of interestingness on Twitter. In Proceedings of the 3rd International Web Science Conference. ACM.

Digital Library

[135]

Anuj Nayak, Seyyedali Hosseinalipour, and Huaiyu Dai. 2019. Smart information spreading for opinion maximization in social networks. In Proceedings of the IEEE Conference on Computer Communications. IEEE, 2251–2259.

[136]

Elmie Nekmat. 2020. Nudge effect of fact-check alerts: Source influence and media skepticism on sharing of news misinformation in social media. Soc. Media + Societ. 6, 1 (2020), 2056305119897322. arXiv:https://doi.org/10.1177/2056305119897322.

[137]

Mark E. J. Newman. 2003. The structure and function of complex networks. SIAM Rev. 45, 2 (2003), 167–256.

Digital Library

[138]

Nam P. Nguyen, Guanhua Yan, and My T. Thai. 2013. Analysis of misinformation containment in online social networks. Comput. Netw. 57, 10 (2013), 2133–2146.

Digital Library

[139]

Vincenzo Nicosia, Per Sebastian Skardal, Alex Arenas, and Vito Latora. 2017. Collective phenomena emerging from the interactions between dynamical processes in multiplex networks. Phys. Rev. Lett. 118, 13 (Mar. 2017), 138302.

[140]

Romualdo Pastor-Satorras, Claudio Castellano, Piet Van Mieghem, and Alessandro Vespignani. 2015. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 3 (Aug. 2015), 925–979.

[141]

Romualdo Pastor-Satorras and Alessandro Vespignani. 2001. Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 14 (Apr. 2001), 3200–3203.

[142]

Hao Peng, Azadeh Nematzadeh, Daniel M. Romero, and Emilio Ferrara. 2020. Network modularity controls the speed of information diffusion. Phys. Rev. E 102, 5 (Nov. 2020), 052316.

[143]

D. Perna, R. Interdonato, and A. Tagarelli. 2018. Identifying users with alternate behaviors of lurking and active participation in multilayer social networks. IEEE Trans. Comput. Soc. Syst. 5, 1 (Mar. 2018), 46–63.

[144]

Diego Perna, Roberto Interdonato, and Andrea Tagarelli. 2018. Learning to lurker rank: An evaluation of learning-to-rank methods for lurking behavior analysis. Soc. Netw. Anal. Mining 8, 1 (2018).

[145]

René Pfitzner, Antonios Garas, and Frank Schweitzer. 2012. Emotional divergence influences information spreading in Twitter. In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media.

[146]

Yadong Qin, Jun Ma, and Shuai Gao. 2017. Efficient influence maximization under TSCM: A suitable diffusion model in online social networks. Soft Comput. 21, 4 (2017), 827–838.

Digital Library

[147]

Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018. DeepInf: Social influence prediction with deep learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2110–2119.

Digital Library

[148]

Marian-Andrei Rizoiu, Lexing Xie, Scott Sanner, Manuel Cebrián, Honglin Yu, and Pascal Van Hentenryck. 2017. Expecting to be HIP: Hawkes intensity processes for social media popularity. In Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3–7, 2017, Rick Barrett, Rick Cummings, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 735–744.

Digital Library

[149]

Agnieszka Rusinowska and Akylai Taalaibekova. 2019. Opinion formation and targeting when persuaders have extreme and centrist opinions. J. Math. Econ. 84 (2019), 9–27.

[150]

Faryad Darabi Sahneh, Caterina M. Scoglio, and Piet Van Mieghem. 2013. Generalized epidemic mean-field model for spreading processes over multilayer complex networks. IEEE ACM Trans. Netw. 21, 5 (2013), 1609–1620.

Digital Library

[151]

Kazumi Saito, Kouzou Ohara, Yuki Yamagishi, Masahiro Kimura, and Hiroshi Motoda. 2011. Learning diffusion probability based on node attributes in social networks. In Proceedings of the International Symposium on Methodologies for Intelligent Systems. Springer, 153–162.

Digital Library

[152]

Aravind Sankar, Xinyang Zhang, Adit Krishnan, and Jiawei Han. 2020. Inf-VAE: A variational autoencoder framework to integrate homophily and influence in diffusion prediction. In Proceedings of the 13th International Conference on Web Search and Data Mining) (WSDM’20). Association for Computing Machinery, New York, NY, 510–518.

Digital Library

[153]

Mike Schroepfer. 2018. An Update on Our Plans to Restrict Data Access on Facebook. Retrieved April 4, 2018 from https://about.fb.com/news/2018/04/restricting-data-access/.

[154]

Emilio Serrano, Carlos Ángel Iglesias, and Mercedes Garijo. 2015. A novel agent-based rumor spreading model in Twitter. In Proceedings of the 24th International Conference on World Wide Web. ACM, 811–814.

Digital Library

[155]

C. Seshadhri, Tamara G. Kolda, and Ali Pinar. 2012. Community structure and scale-free collections of Erdős-Rényi graphs. Phys. Rev. E 85, 5 (May 2012), 056109.

[156]

C. Seshadhri, Ali Pinar, and Tamara G. Kolda. 2013. Triadic Measures on Graphs: The Power of Wedge Sampling. SIAM, 10–18. https://doi.org/10.1137/1.9781611972832.2

[157]

Huawei Shen, Dashun Wang, Chaoming Song, and Albert-László Barabási. 2014. Modeling and predicting popularity dynamics via reinforced Poisson processes. In Proceedings of the 28th AAAI Conference on Artificial Intelligence.

Digital Library

[158]

Jieun Shin, Lian Jian, Kevin Driscoll, and Frans Bar. 2018. The diffusion of misinformation on social media: Temporal pattern, message, and source. Comput. Hum. Behav. 83 (2018), 278–287.

Digital Library

[159]

Jieun Shin and Kjerstin Thorson. 2017. Partisan selective sharing: The biased diffusion of fact-checking messages on social media. J. Commun. 67, 2 (02 2017), 233–255. arXiv:https://academic.oup.com/joc/article-pdf/67/2/233/22321279/jjnlcom0233.pdf.

[160]

Omid Askari Sichani and Mahdi Jalili. 2017. Inference of hidden social power through opinion formation in complex networks. IEEE Trans. Netw. Sci. Eng. 4, 3 (2017), 154–164.

[161]

Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.).

[162]

Shashank Sheshar Singh, Kuldeep Singh, Ajay Kumar, Harish Kumar Shakya, and Bhaskar Biswas. 2018. A survey on information diffusion models in social networks. In Proceedings of the International Conference on Advanced Informatics for Computing Research. Springer, 426–439.

[163]

E. E. Smith and S. M. Kosslyn. 2008. Cognitive Psychology: Mind and Brain. Pearson Prentice Hall. Retrieved from https://books.google.com.tw/books?id=YIPSNwAACAAJ.

[164]

Eleni Stai, Eirini Milaiou, Vasileios Karyotis, and Symeon Papavassiliou. 2018. Temporal dynamics of information diffusion in Twitter: Modeling and experimentation. IEEE Trans. Comput. Soc. Syst. 5, 1 (2018), 256–264.

[165]

Natalie Jomini Stroud. 2010. Polarization and partisan selective exposure. J. Commun. 60, 3 (08 2010), 556–576. arXiv:https://academic.oup.com/joc/article-pdf/60/3/556/22324536/jjnlcom0556.pdf.

[166]

Yuan Su, Xi Zhang, Senzhang Wang, Binxing Fang, Tianle Zhang, and Philip S. Yu. 2019. Understanding information diffusion via heterogeneous information network embeddings. In Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22–25, 2019, Proceedings, Part I(Lecture Notes in Computer Science, Vol. 11446), Guoliang Li, Jun Yang, João Gama, Juggapong Natwichai, and Yongxin Tong (Eds.). Springer, 501–516.

[167]

Bongwon Suh, Lichan Hong, Peter Pirolli, and Ed H. Chi. 2010. Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In Proceedings of the IEEE 2nd International Conference on Social Computing. IEEE, 177–184.

Digital Library

[168]

Anjana Susarla, Jeong-Ha Oh, and Yong Tan. 2012. Social networks and the diffusion of user-generated content: Evidence from YouTube. Inf. Syst. Res. 23, 1 (2012), 23–41. Retrieved from _eprint: https://pubsonline.informs.org/doi/pdf/10.1287/isre.1100.0339.

Digital Library

[169]

Andrea Tagarelli and Roberto Interdonato. 2014. Lurking in social networks: Topology-based analysis and ranking methods. Soc. Netw. Anal. Mining 4, 1 (2014).

[170]

Andrea Tagarelli and Roberto Interdonato. 2015. Time-aware analysis and ranking of lurkers in social networks. Soc. Netw. Anal. Mining 5, 1 (2015), 46.

[171]

Andrea Tagarelli and Roberto Interdonato. 2018. Lurking behavior analysis. In Mining Lurkers in Online Social Networks: Principles, Models, and Computational Methods. Springer International Publishing, Cham, 29–38.

Digital Library

[172]

Marcella Tambuscio and Giancarlo Ruffo. 2019. Fact-checking strategies to limit urban legends spreading in a segregated society. Appl. Netw. Sci. 4, 1 (2019).

[173]

Marcella Tambuscio, Giancarlo Ruffo, Alessandro Flammini, and Filippo Menczer. 2015. Fact-checking effect on viral hoaxes: A model of misinformation spread in social networks. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). Association for Computing Machinery, New York, NY, 977–982.

Digital Library

[174]

Jie Tang. 2006. Datasets for Social Network Analysis. Retrieved from https://www.aminer.cn/data-sna.

[175]

Jiliang Tang, Huiji Gao, and Huan Liu. 2012. mTrust: Discerning multi-faceted trust in a connected world. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining. ACM, 93–102.

Digital Library

[176]

Jiliang Tang, Xia Hu, and Huan Liu. 2013. Social recommendation: A review. Soc. Netw. Anal. Mining 3, 4 (1 Jan. 2013), 1113–1133.

[177]

Ye Tian and Long Wang. 2018. Opinion dynamics in social networks with stubborn agents: An issue-based perspective. Automatica 96 (2018), 213–223.

Digital Library

[178]

G. Tong, W. Wu, L. Guo, D. Li, C. Liu, B. Liu, and D. Du. 2020. An efficient randomized algorithm for rumor blocking in online social networks. IEEE Trans. Netw. Sci. Eng. 7, 2 (2020), 845–854.

[179]

Oren Tsur and Ari Rappoport. 2012. What’s in a hashtag?: Content based prediction of the spread of ideas in microblogging communities. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining. ACM, 643–652.

Digital Library

[180]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, undefinedukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Curran Associates Inc., Red Hook, NY, 6000–6010.

Digital Library

[181]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations.

[182]

Vlado. 2010. Network Data Sources. Retrieved from http://vladowiki.fmf.uni-lj.si/doku.php?id=pajek:data:urls:index.

[183]

Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146–1151.

[184]

Duy Q. Vu, Arthur U. Asuncion, David R. Hunter, and Padhraic Smyth. 2011. Dynamic egocentric models for citation networks. In Proceedings of the 28th International Conference on International Conference on Machine Learning (ICML’11). Omnipress, 857–864. Retrieved from http://dl.acm.org/citation.cfm?id=3104482.3104590.

Digital Library

[185]

Jacco Wallinga and Peter Teunis. 2004. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Amer. J. Epidemiol. 160, 6 (09 2004), 509–516. arXiv:https://academic.oup.com/aje/article-pdf/160/6/509/179728/kwh255.pdf.

[186]

Feng Wang, Haiyan Wang, and Kuai Xu. 2012. Diffusive logistic model towards predicting information diffusion in online social networks. In Proceedings of the 32nd International Conference on Distributed Computing Systems Workshops. IEEE, 133–139.

Digital Library

[187]

Jia Wang, Vincent W. Zheng, Zemin Liu, and Kevin Chen-Chuan Chang. 2017. Topological recurrent neural network for diffusion prediction. In 2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18–21, 2017, Vijay Raghavan, Srinivas Aluru, George Karypis, Lucio Miele, and Xindong Wu (Eds.). IEEE Computer Society, 475–484.

[188]

Liaoruo Wang, Stefano Ermon, and John E. Hopcroft. 2012. Feature-enhanced probabilistic models for diffusion network inference. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 499–514.

Digital Library

[189]

Senzhang Wang, Xia Hu, Philip S. Yu, and Zhoujun Li. 2014. MMRate: Inferring multi-aspect diffusion networks with multi-pattern cascades. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1246–1255.

Digital Library

[190]

Tianbo Wang, Chunhe Xia, Zhong Li, Xiaochen Liu, and Yang Xiang. 2017. The spatial–temporal perspective: The study of the propagation of modern social worms. IEEE Trans. Inf. Forens. Secur. 12, 11 (2017), 2558–2573.

Digital Library

[191]

Tianbo Wang, Chunhe Xia, Sheng Wen, Hui Xue, Yang Xiang, and Shouzhong Tu. 2017. Sadi: A novel model to study the propagation of social worms in hierarchical networks. IEEE Trans. Depend. Secure Comput. 16, 1 (2017), 142–155.

Digital Library

[192]

Wen Wang, Wei Zhang, and Jun Wang. 2018. Factorization meets memory network: Learning to predict activity popularity. In Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21–24, 2018, Proceedings, Part II(Lecture Notes in Computer Science, Vol. 10828), Jian Pei, Yannis Manolopoulos, Shazia W. Sadiq, and Jianxin Li (Eds.). Springer, 509–525.

[193]

Xin Wang, Weihua Li, Longzhao Liu, Sen Pei, Shaoting Tang, and Zhiming Zheng. 2017. Promoting information diffusion through interlayer recovery processes in multiplex networks. Phys. Rev. E 96, 3 (Sep 2017), 032304.

[194]

Yini Wang, Sheng Wen, Yang Xiang, and Wanlei Zhou. 2013. Modeling the propagation of worms in networks: A survey. IEEE Commun. Surv. Tutor. 16, 2 (2013), 942–960.

[195]

Ya-Qi Wang, Xiao-Yuan Yang, Yi-Liang Han, and Xu-An Wang. 2013. Rumor spreading model with trust mechanism in complex social networks. Commun. Theoret. Phys. 59, 4 (Apr. 2013), 510–516.

[196]

Audrey Watters. 2011. How Recent Changes to Twitter’s Terms of Service Might Hurt Academic Research. https://readwrite.com/2011/03/03/how_recent_changes_to_twitters_terms_of_service_mi/. Accessed March 3, 2011.

[197]

Lilian Weng, Jacob Ratkiewicz, Nicola Perra, Bruno Gonçalves, Carlos Castillo, Francesco Bonchi, Rossano Schifanella, Filippo Menczer, and Alessandro Flammini. 2013. The role of information diffusion in the evolution of social networks. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). ACM, New York, NY, 356–364.

Digital Library

[198]

Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, and Tao Mei. 2017. Sequential prediction of social media popularity with deep temporal context networks. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19–25, 2017, Carles Sierra (Ed.). ijcai.org, 3062–3068.

Digital Library

[199]

Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang. 2019. A neural influence diffusion model for social recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). Association for Computing Machinery, New York, NY, 235–244.

Digital Library

[200]

Mike Wu and Noah Goodman. 2018. Multimodal generative models for scalable weakly-supervised learning. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18). Curran Associates Inc., Red Hook, NY, 5580–5590.

Digital Library

[201]

Qitian Wu, Chaoqi Yang, Hengrui Zhang, Xiaofeng Gao, Paul Weng, and Guihai Chen. 2018. Adversarial training model unifying feature driven and point process perspectives for event popularity prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 517–526.

Digital Library

[202]

Jiayi Xie, Yaochen Zhu, Zhibin Zhang, Jian Peng, Jing Yi, Yaosi Hu, Hongyi Liu, and Zhenzhong Chen. 2020. A multimodal variational encoder-decoder framework for micro-video popularity prediction. In WWW’20: The Web Conference 2020, Taipei, Taiwan, April 20–24, 2020, Yennun Huang, Irwin King, Tie-Yan Liu, and Maarten van Steen (Eds.). ACM / IW3C2, 2542–2548.

Digital Library

[203]

Miao Xie, Qiusong Yang, Qing Wang, Gao Cong, and Gerard De Melo. 2015. DynaDiffuse: A dynamic diffusion model for continuous time constrained influence maximization. In Proceedings of the 29th AAAI Conference on Artificial Intelligence.

Digital Library

[204]

Qichao Xu, Zhou Su, Kuan Zhang, Pinyi Ren, and Xuemin Sherman Shen. 2015. Epidemic information dissemination in mobile social networks with opportunistic links. IEEE Trans. Emerg. Topics Comput. 3, 3 (2015), 399–409.

[205]

Shaobin Xu and David A. Smith. 2018. Contrastive training for models of information cascades. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.

Digital Library

[206]

Cheng Yang, Maosong Sun, Haoran Liu, Shiyi Han, Zhiyuan Liu, and Huanbo Luan. 2021. Neural diffusion model for microscopic cascade study. IEEE Trans. Knowl. Data Eng. 33, 3 (2021), 1128–1139.

[207]

Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, and Zhiyuan Liu. 2019. Multi-scale information diffusion prediction with reinforced recurrent networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10–16, 2019, Sarit Kraus (Ed.). ijcai.org, 4033–4039.

Digital Library

[208]

Jiang Yang and Scott Counts. 2010. Comparing information diffusion structure in weblogs and microblogs. In Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, USA, May 23–26, 2010, William W. Cohen and Samuel Gosling (Eds.). The AAAI Press. Retrieved from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1467.

[209]

Jiang Yang and Scott Counts. 2010. Predicting the speed, scale, and range of information diffusion in Twitter. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media.

[210]

Jaewon Yang and Jure Leskovec. 2010. Modeling information diffusion in implicit networks. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 599–608.

Digital Library

[211]

Jaewon Yang and Jure Leskovec. 2011. Patterns of temporal variation in online media. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. ACM, 177–186.

Digital Library

[212]

Lan Yang, Zhiwu Li, and Alessandro Giua. 2020. Containment of rumor spread in complex social networks. Inf. Sci. 506 (2020), 113–130.

Digital Library

[213]

Lu-Xing Yang, Xiaofan Yang, and Yuan Yan Tang. 2017. A bi-virus competing spreading model with generic infection rates. IEEE Trans. Netw. Sci. Eng. 5, 1 (2017), 2–13.

[214]

Yang Yang, Jie Tang, Cane Wing-ki Leung, Yizhou Sun, Qicong Chen, Juanzi Li, and Qiang Yang. 2015. RAIN: Social role-aware information diffusion. In AAAI, Vol. 15. 367–373.

Digital Library

[215]

Zi Yang, Jingyi Guo, Keke Cai, Jie Tang, Juanzi Li, Li Zhang, and Zhong Su. 2010. Understanding retweeting behaviors in social networks. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10). Association for Computing Machinery, New York, NY, 1633–1636.

Digital Library

[216]

Qipeng Yao, Xu Wu, and Xi Zhang. 2015. Diffusion of information in mobile social networks: A brief survey. In Proceedings of the IEEE International Conference on Mobile Services. IEEE, 254–260.

Digital Library

[217]

Dannagal G. Young, Kathleen Hall Jamieson, Shannon Poulsen, and Abigail Goldring. 2018. Fact-checking effectiveness as a function of format and tone: Evaluating FactCheck.org and FlackCheck.org. J. Mass Commun. Quart. 95, 1 (2018), 49–75. arXiv:https://doi.org/10.1177/1077699017710453.

[218]

Linyun Yu, Peng Cui, Fei Wang, Chaoming Song, and Shiqiang Yang. 2015. From micro to macro: Uncovering and predicting information cascading process with behavioral dynamics. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 559–568.

Digital Library

[219]

Nicholas Jing Yuan, Yuan Zhong, Fuzheng Zhang, Xing Xie, Chin-Yew Lin, and Yong Rui. 2016. Who will reply to/retweet this tweet? The dynamics of intimacy from online social interactions. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. ACM, 3–12.

Digital Library

[220]

Ali Zarezade, Ali Khodadadi, Mehrdad Farajtabar, Hamid R. Rabiee, and Hongyuan Zha. 2017. Correlated cascades: Compete or cooperate. In Proceedings of the 31st AAAI Conference on Artificial Intelligence.

Digital Library

[221]

Jing Zhang, Biao Liu, Jie Tang, Ting Chen, and Juanzi Li. 2013. Social influence locality for modeling retweeting behaviors. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence.

Digital Library

[222]

Jing Zhang, Jie Tang, Juanzi Li, Yang Liu, and Chunxiao Xing. 2015. Who influenced you? Predicting retweet via social influence locality. ACM Trans. Knowl. Discov. Data 9, 3 (2015).

Digital Library

[223]

Jiawei Zhang, Philip S. Yu, Yuanhua Lv, and Qianyi Zhan. 2016. Information diffusion at workplace. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management. ACM, 1673–1682.

Digital Library

[224]

Qi Zhang, Yeyun Gong, Jindou Wu, Haoran Huang, and Xuanjing Huang. 2016. Retweet prediction with attention-based deep neural network. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management. ACM, 75–84.

Digital Library

[225]

Wei Zhang, Wen Wang, Jun Wang, and Hongyuan Zha. 2018. User-guided hierarchical attention network for multi-modal social image popularity prediction. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23–27, 2018, Pierre-Antoine Champin, Fabien L. Gandon, Mounia Lalmas, and Panagiotis G. Ipeirotis (Eds.). ACM, 1277–1286.

Digital Library

[226]

Laijun Zhao, Jiajia Wang, Yucheng Chen, Qin Wang, Jingjing Cheng, and Hongxin Cui. 2012. SIHR rumor spreading model in social networks. Phys. A: Statist. Mech. Applic. 391, 7 (2012), 2444–2453.

[227]

Qingyuan Zhao, Murat A. Erdogdu, Hera Y. He, Anand Rajaraman, and Jure Leskovec. 2015. Seismic: A self-exciting point process model for predicting tweet popularity. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1513–1522.

Digital Library

[228]

Chunyuan Zheng, Chengyi Xia, Quantong Guo, and Matthias Dehmer. 2018. Interplay between SIR-based disease spreading and awareness diffusion on multiplex networks. J. Parallel Distrib. Comput. 115 (2018), 20–28.

Digital Library

[229]

Fan Zhou, Xovee Xu, Goce Trajcevski, and Kunpeng Zhang. 2021. A survey of information cascade analysis: Models, predictions, and recent advances. ACM Comput. Surv. 54, 2 (Mar. 2021).

Digital Library

[230]

Fan Zhou, Xovee Xu, Kunpeng Zhang, Goce Trajcevski, and Ting Zhong. 2020. Variational information diffusion for probabilistic cascades prediction. In Proceedings of the 39th IEEE Conference on Computer Communications. IEEE, 1618–1627.

Digital Library

[231]

Jie Zhou, Zonghua Liu, and Baowen Li. 2007. Influence of network structure on rumor propagation. Phys. Lett. A 368, 6 (2007), 458–463.

[232]

Hui Zhu, Cheng Huang, and Hui Li. 2015. Information diffusion model based on privacy setting in online social networking services. Comput. J. 58, 4 (2015), 536–548.

[233]

Liang Zhu and Youguo Wang. 2017. Rumor spreading model with noise interference in complex social networks. Phys. A: Statist. Mech. Applic. 469 (2017), 750–760.

[234]

Liang Zhu and Youguo Wang. 2018. Rumor diffusion model with spatio-temporal diffusion and uncertainty of behavior decision in complex social networks. Phys. A: Statist. Mech. Applic. 502 (2018), 29–39.

[235]

Linhe Zhu, Hongyong Zhao, and Haiyan Wang. 2016. Complex dynamic behavior of a rumor propagation model with spatial-temporal diffusion terms. Inf. Sci. 349-350 (2016), 119–136.

Digital Library

[236]

Yuqing Zhu, Deying Li, and Zhao Zhang. 2016. Minimum cost seed set for competitive social influence. In Proceedings of the IEEE 35th Annual IEEE International Conference on Computer Communications. IEEE, 1–9.

Digital Library

[237]

Wei Zhuo, Yanan Zhao, Qianyi Zhan, and Yuan Liu. 2019. DiffusionGAN: Network embedding for information diffusion prediction with generative adversarial nets. In Proceedings of the IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking. IEEE, 808–816.

[238]

Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. 2018. Detection and resolution of rumours in social media: A survey. ACM Comput. Surv. 51, 2 (Feb. 2018).

Digital Library

Cited By

Lalor JAbbasi AOketch KYang YForsgren N(2024)Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning PipelinesACM Transactions on Information Systems10.1145/364127642:4(1-41)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3641276
Kou T(2024)From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility GapProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658951(1002-1013)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658951
Li HXia CWang TLin WJiang CChen CZhao Y(2024)Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic ConsistencyICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448010(5450-5454)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448010
Show More Cited By

Index Terms

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social networks

Recommendations

Information diffusion on Facebook: a case study of the sunflower student movement in Taiwan
IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

On March 18, 2014, some students occupied the Taiwanese Parliament Building in order to resume the negotiation for the Cross-Strait Service Trade Agreement (CSSTA). They posted as well as shared posts on Facebook and even created fan pages on Facebook ...
Strategic information diffusion through online social networks
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies

The emergence of online social network platforms enable users to exchange and diffuse information in a more complex way. Different from being just a relay as in traditional diffusion systems, online social network users can even append their ideas on ...
Information Diffusion Model Based on Social Big Data

Users can access Weibo, a widely used social network platform, through mobile clients or PCs at any time for social interaction. This paper describes an information diffusion model based on the Weibo platform, which is used to measure information ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 1

January 2023

860 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3492451

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 November 2021

Accepted: 01 September 2021

Revised: 01 July 2021

Received: 01 August 2020

Published in CSUR Volume 55, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Refereed

Funding Sources

National Natural Science Foundation of China
Beihang Youth Top Talent Support Program

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
1,635
Total Downloads

Downloads (Last 12 months)521
Downloads (Last 6 weeks)43

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lalor JAbbasi AOketch KYang YForsgren N(2024)Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning PipelinesACM Transactions on Information Systems10.1145/364127642:4(1-41)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3641276
Kou T(2024)From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility GapProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658951(1002-1013)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658951
Li HXia CWang TLin WJiang CChen CZhao Y(2024)Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic ConsistencyICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448010(5450-5454)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448010
Yu DZhou YZhang SLi WSmall MShang K(2024)Information cascade prediction of complex networks based on physics-informed graph convolutional networkNew Journal of Physics10.1088/1367-2630/ad1b2926:1(013031)Online publication date: 17-Jan-2024
https://doi.org/10.1088/1367-2630/ad1b29
Upreti RLind PElmokashfi AYazidi A(2024)Trustworthy machine learning in the context of security and privacyInternational Journal of Information Security10.1007/s10207-024-00813-323:3(2287-2314)Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1007/s10207-024-00813-3
Emadi MRahgozar MOroumchian F(2023)Inferring Diffusion Network from Information Cascades using Transitive InfluenceJournal of Information Systems and Telecommunication (JIST)10.61186/jist.33656.11.44.30711:44(307-319)Online publication date: 16-Dec-2023
https://doi.org/10.61186/jist.33656.11.44.307
Zahirnia KHu YCoates MSchulte OOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Neural graph generation from graph statisticsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667698(36324-36338)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667698
Chen ZXu BCai TYang ZLiao X(2023)A Dynamic Emotional Propagation Model over Time for Competitive EnvironmentsElectronics10.3390/electronics1224493712:24(4937)Online publication date: 8-Dec-2023
https://doi.org/10.3390/electronics12244937
Masotina MMusi ESpagnolli A(2023)Transparency is Crucial for User-Centered AI, or is it? How this Notion Manifests in the UK Press Coverage of GPTProceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter10.1145/3605390.3605413(1-8)Online publication date: 20-Sep-2023
https://dl.acm.org/doi/10.1145/3605390.3605413
Du SFang ZLan STan YGünther MWang SGuo WEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and RobustnessProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612352(8719-8729)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612352
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents