research-article

Incorporating a Triple Graph Neural Network with Multiple Implicit Feedback for Social Recommendation

Authors:

Liang WangAuthors Info & Claims

ACM Transactions on the Web, Volume 18, Issue 2

Article No.: 23, Pages 1 - 26

https://doi.org/10.1145/3580517

Published: 08 January 2024 Publication History

Abstract

Graph neural networks have been clearly proven to be powerful in recommendation tasks since they can capture high-order user-item interactions and integrate them with rich attributes. However, they are still limited by the cold-start problem and data sparsity. Using social relationships to assist recommendation is an effective practice, but it can only moderately alleviate these problems. In addition, rich attributes are often unavailable, which prevents graph neural networks from being fully effective. Hence, we propose to enrich the model by mining multiple implicit feedback and constructing a triple GCN component. We have noticed that users may be influenced not only by their trusted friends but also by the ratings that already exist. The implicit influence spreads among the item’s previous and potential raters, and makes a difference on future ratings. The implicit influence is analyzed on the mechanism of information propagation, and fused with the user’s binary implicit attitude, since negative influence propagates as well as the positive one. Furthermore, we leverage explicit feedback, social relationships, and multiple implicit feedback in the triple GCN component. Abundant experiments on real-world datasets reveal that our model has improved significantly in the rating prediction task compared with other state-of-the-art methods.

References

[1]

Nicolas Gillis. 2020. Computational complexity of NMF. In Nonnegative Matrix Factorization. SIAM, 195–205. DOI:

[2]

Zeinab Abbassi and Vahab S. Mirrokni. 2007. A recommender system based on local random walks and spectral methods. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. 102–108.

Digital Library

[3]

Saad Albawi, Tareq Abed Mohammed, and Saad Al-Zawi. 2017. Understanding of a convolutional neural network. In Proceedings of the 2017 International Conference on Engineering and Technology (ICET’17). IEEE, Los Alamitos, CA, 1–6.

[4]

Faris Alqadah, Chandan K. Reddy, Junling Hu, and Hatim F. Alqadah. 2015. Biclustering neighborhood-based collaborative filtering method for top-n recommender systems. Knowledge and Information Systems 44, 2 (2015), 475–491.

Digital Library

[5]

Ching-Man Au Yeung and Tomoharu Iwata. 2011. Strength of social influence in trust networks in product review sites. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 495–504.

Digital Library

[6]

Ting Bai, Jian-Yun Nie, Wayne Xin Zhao, Yutao Zhu, Pan Du, and Ji-Rong Wen. 2018. An attribute-aware neural attentive model for next basket recommendation. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 1201–1204.

Digital Library

[7]

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013).

[8]

Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, and Jie Tang. 2019. Representation learning for attributed multiplex heterogeneous network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1358–1368.

Digital Library

[9]

Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. 2020. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 3438–3445.

[10]

Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018).

[11]

Min Chen. 2018. Improving website structure through reducing information overload. Decision Support Systems 110 (2018), 84–94.

[12]

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, et al. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 7–10.

Digital Library

[13]

Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 257–266.

Digital Library

[14]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems 29 (2016), 3844–3852.

Digital Library

[15]

Christian Desrosiers and George Karypis. 2011. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor (Eds.). Springer, Boston, MA, 107–144.

[16]

Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, and Yongliang Li. 2019. Metapath-guided heterogeneous graph neural network for intent recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2478–2486.

Digital Library

[17]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In Proceedings of the World Wide Web Conference. 417–426.

Digital Library

[18]

Maryam Fatemi and Laurissa Tokarchuk. 2013. A community based social recommender system for individuals & groups. In Proceedings of the 2013 International Conference on Social Computing. IEEE, Los Alamitos, CA, 351–356.

Digital Library

[19]

Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for quantum chemistry. In Proceedings of the International Conference on Machine Learning. 1263–1272.

[20]

Yoav Goldberg and Omer Levy. 2014. Word2vec explained: Deriving Mikolov et al.’s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 (2014).

[21]

Mark Granovetter. 1978. Threshold models of collective behavior. American Journal of Sociology 83, 6 (1978), 1420–1443.

[22]

Guibing Guo, Jie Zhang, and Neil Yorke-Smith. 2016. A novel recommendation model regularized with user trust and item ratings. IEEE Transactions on Knowledge and Data Engineering 28, 7 (2016), 1607–1620.

[23]

William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025–1035.

Digital Library

[24]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. 173–182.

Digital Library

[25]

Chin-Chi Hsu, Mi-Yen Yeh, and Shou-De Lin. 2018. A general framework for implicit and explicit social recommendation. IEEE Transactions on Knowledge and Data Engineering 30, 12 (2018), 2228–2241.

Digital Library

[26]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 8th IEEE International Conference on Data Mining. IEEE, Los Alamitos, CA, 263–272.

Digital Library

[27]

Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020. Heterogeneous graph transformer. In Proceedings of the Web Conference 2020. 2704–2710.

Digital Library

[28]

Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the 4th ACM Conference on Recommender Systems. 135–142.

Digital Library

[29]

Anne-Marie Kermarrec, Vincent Leroy, Afshin Moin, and Christopher Thraves. 2010. Application of random walks to decentralized recommender systems. In Proceedings of the International Conference on Principles of Distributed Systems. 48–63.

[30]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[31]

Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[32]

Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 426–434.

Digital Library

[33]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.

Digital Library

[34]

D. D. Lee and H. S. Seung. 2000. Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems 13 (2000), 556–562.

[35]

Jianxin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip Yu, and Lifang He. 2021. Higher-order attribute-enhancing heterogeneous graph neural networks. IEEE Transactions on Knowledge and Data Engineering 35 (2021), 560–574.

[36]

Hai Liang. 2018. Broadcast versus viral spreading: The structure of diffusion cascades and selective sharing on social media. Journal of Communication 68, 3 (2018), 525–546.

[37]

Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, and Philip S. Yu. 2020. Deep learning for community detection: Progress, challenges and opportunities. arXiv preprint arXiv:2005.08225 (2020).

[38]

Nathan N. Liu, Evan W. Xiang, Min Zhao, and Qiang Yang. 2010. Unifying explicit and implicit feedback for collaborative filtering. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 1445–1448.

Digital Library

[39]

Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2011. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor (Eds.). Springer, Boston, MA, 73–105.

[40]

Hao Ma, Irwin King, and Michael R. Lyu. 2009. Learning to recommend with social trust ensemble. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 203–210.

Digital Library

[41]

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. 931–940.

Digital Library

[42]

Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Quan Z. Sheng, and Hui Xiong. 2021. A comprehensive survey on graph anomaly detection with deep learning. arXiv preprint arXiv:2106.07178 (2021).

[43]

Larry R. Medsker and L. C. Jain. 2001. Recurrent neural networks. Design and Applications 5 (2001), 64–67.

[44]

Steven J. Nowlan and Geoffrey E. Hinton. 2018. Simplifying neural networks by soft weight sharing. In The Mathematics of Generalization. CRC Press, Boca Raton, FL, 373–394.

[45]

Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, and Philip S. Yu. 2021. Reinforced neighborhood selection guided multi-relational graph neural networks. ACM Transactions on Information Systems 40, 4 (2021), 1–46.

Digital Library

[46]

Lisa Rashotte. 2007. Social influence. The Blackwell Encyclopedia of Sociology, 4426-4429.

[47]

Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017).

[48]

Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009 (2009), Article 4, 2 pages.

Digital Library

[49]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, et al. 2021. A comprehensive survey on community detection with deep learning. arXiv preprint arXiv:2105.12584 (2021).

[50]

Jiliang Tang, Xia Hu, and Huan Liu. 2013. Social recommendation: A review. Social Network Analysis and Mining 3, 4 (2013), 1113–1133.

[51]

Andreas Töscher, Michael Jahrer, and Robert Legenstein. 2008. Improved neighborhood-based algorithms for large-scale recommender systems. In Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition. 1–6.

Digital Library

[52]

John C. Turner. 1991. Social Influence. Thomson Brooks/Cole Publishing Co.

[53]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 5998–6008.

[54]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).

[55]

M. Vijaikumar, Shirish Shevade, and M. Narasimha Murty. 2020. Neural cross-domain collaborative filtering with shared entities. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 729–745.

[56]

Donghui Wang, Yanchun Liang, Dong Xu, Xiaoyue Feng, and Renchu Guan. 2018. A content-based recommender system for computer science publications. Knowledge-Based Systems 157 (2018), 1–9.

[57]

Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, and Meng Wang. 2020. DiffNet++: A neural influence and interest diffusion network for social recommendation. IEEE Transactions on Knowledge and Data Engineering 34, 10 (2020), 4753–4766.

[58]

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. 235–244.

Digital Library

[59]

Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In Proceedings of the World Wide Web Conference. 2091–2102.

Digital Library

[60]

Shiwen Wu, Fei Sun, Wentao Zhang, and Bin Cui. 2020. Graph neural networks in recommender systems: A survey. arXiv preprint arXiv:2011.02260 (2020).

[61]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S. Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2020), 4–24.

[62]

Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-Ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In Proceedings of the International Conference on Machine Learning. 5453–5462.

[63]

Bo Yang, Yu Lei, Dayou Liu, and Jiming Li. 2013. Social collaborative filtering by trust. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2747–2753.

Digital Library

[64]

Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 974–983.

Digital Library

[65]

Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019. GraphSAINT: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931 (2019).

[66]

Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477 (2019).

[67]

Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V. Chawla. 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 793–803.

Digital Library

[68]

Chuxu Zhang, Lu Yu, Yan Wang, Chirag Shah, and Xiangliang Zhang. 2017. Collaborative user network embedding for social recommender systems. In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM’17). 381–389. DOI:

[69]

Jia-Dong Zhang, Chi-Yin Chow, and Jin Xu. 2016. Enabling kernel-based attribute-aware matrix factorization for rating prediction. IEEE Transactions on Knowledge and Data Engineering 29, 4 (2016), 798–812.

Digital Library

Cited By

Zhang LJian XMa Y(2024)Analysis of differences in fossil fuel consumption in the world based on the fractal time series and complex networkFrontiers in Physics10.3389/fphy.2024.145728712Online publication date: 2-Oct-2024
https://doi.org/10.3389/fphy.2024.1457287
Liu MGuo QLiu J(2024)Effect of network structure on the accuracy of resilience dimension reductionFrontiers in Physics10.3389/fphy.2024.142055612Online publication date: 19-Jun-2024
https://doi.org/10.3389/fphy.2024.1420556
Hao JMa JLiu STian Y(2024)A novel spreading dynamic based on adoption against the trendFrontiers in Physics10.3389/fphy.2024.140640312Online publication date: 12-Jun-2024
https://doi.org/10.3389/fphy.2024.1406403
Show More Cited By

Index Terms

Incorporating a Triple Graph Neural Network with Multiple Implicit Feedback for Social Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

Recommendations

Federated Social Recommendation with Graph Neural Network
Recommender systems have become prosperous nowadays, designed to predict users’ potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems (RSs) with powerful backbones to ...
BiasRec: A General Bias-Aware Social Recommendation Model
Database Systems for Advanced Applications
Abstract
The social recommendation aims to alleviate data sparsity problems and improve recommendation performance by incorporating user social data. And the recent popularity of graph neural networks (GNNs) has further advanced the development of social ...
Social Recommendation with Implicit Social Influence
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Social influence is essential to social recommendation. Current influence-based social recommendation focuses on the explicit influence on observed social links. However, in real cases, implicit social influence can also impact users' preference in an ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 18, Issue 2

May 2024

378 pages

EISSN:1559-114X

DOI:10.1145/3613666

Editor:
White Ryen
Microsoft Research, USA

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 the author(s) 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: 08 January 2024

Online AM: 21 January 2023

Accepted: 03 December 2022

Revised: 27 October 2022

Received: 31 January 2022

Published in TWEB Volume 18, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Beijing Nova Program from Beijing Municipal Science & Technology Commission

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
645
Total Downloads

Downloads (Last 12 months)366
Downloads (Last 6 weeks)31

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang LJian XMa Y(2024)Analysis of differences in fossil fuel consumption in the world based on the fractal time series and complex networkFrontiers in Physics10.3389/fphy.2024.145728712Online publication date: 2-Oct-2024
https://doi.org/10.3389/fphy.2024.1457287
Liu MGuo QLiu J(2024)Effect of network structure on the accuracy of resilience dimension reductionFrontiers in Physics10.3389/fphy.2024.142055612Online publication date: 19-Jun-2024
https://doi.org/10.3389/fphy.2024.1420556
Hao JMa JLiu STian Y(2024)A novel spreading dynamic based on adoption against the trendFrontiers in Physics10.3389/fphy.2024.140640312Online publication date: 12-Jun-2024
https://doi.org/10.3389/fphy.2024.1406403
Lin HZhang Z(2024)Modeling the Social Influence Effectiveness of Popular Weibo EventsComplexity10.1155/2024/64571152024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/6457115
Zeng RFang JLiu SMeng ZLiang S(2024)Enhancing Graph Neural Networks via Memorized Global InformationACM Transactions on the Web10.1145/3689430Online publication date: 28-Aug-2024
https://doi.org/10.1145/3689430
Zhang QRen SLi XFeng XLi QRen ZMa X(2024)A service-recommendation method for the Internet of Things leveraging implicit social relationshipsComputers and Electrical Engineering10.1016/j.compeleceng.2024.109734120(109734)Online publication date: Dec-2024
https://doi.org/10.1016/j.compeleceng.2024.109734
Wang ZChen JLi JWang Z(2024)Interest community-based recommendation via cognitive similarity and adaptive evolutionary clusteringChaos, Solitons & Fractals10.1016/j.chaos.2024.115085185(115085)Online publication date: Aug-2024
https://doi.org/10.1016/j.chaos.2024.115085
Hoda NAhmad NAldweesh ANaveed Q(2023)Intensity of SNS Use as a Predictor of Online Social Capital and the Moderating Role of SNS Platforms: An Empirical Study Using Partial Least Squares Structural Equation ModellingSustainability10.3390/su1506496715:6(4967)Online publication date: 10-Mar-2023
https://doi.org/10.3390/su15064967
Zhang LZhang WWu LHe MZhao H(2023)SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-behavior PredictionACM Transactions on the Web10.1145/361751018:1(1-27)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3617510
Ye HSong YLi MCao F(2023)A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10343960:5Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103439

View Options

Get Access

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.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents