research-article

SigGAN: Adversarial Model for Learning Signed Relationships in Networks

Authors:

Roshni Chakraborty,

Joydeep ChandraAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 1

Article No.: 8, Pages 1 - 20

https://doi.org/10.1145/3532610

Published: 20 February 2023 Publication History

Abstract

Signed link prediction in graphs is an important problem that has applications in diverse domains. It is a binary classification problem that predicts whether an edge between a pair of nodes is positive or negative. Existing approaches for link prediction in unsigned networks cannot be directly applied for signed link prediction due to their inherent differences. Furthermore, signed link prediction must consider the inherent characteristics of signed networks, such as structural balance theory. Recent signed link prediction approaches generate node representations using either generative models or discriminative models. Inspired by the recent success of Generative Adversarial Network (GAN) based models in several applications, we propose a GAN based model for signed networks, SigGAN. It considers the inherent characteristics of signed networks, such as integration of information from negative edges, high imbalance in number of positive and negative edges, and structural balance theory. Comparing the performance with state-of-the-art techniques on five real-world datasets validates the effectiveness of SigGAN.

References

[1]

Priyanka Agrawal, Vikas K. Garg, and Ramasuri Narayanam. 2013. Link label prediction in signed social networks. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2591–2597.

[2]

Ghazaleh Beigi, Jiliang Tang, and Huan Liu. 2016. Signed link analysis in social media networks. In Proceedings of the International AAAI Conference on Web and Social Media. 539–542.

[3]

Ayan Kumar Bhowmick, Koushik Meneni, and Bivas Mitra. 2019. On the network embedding in sparse signed networks. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 94–106.

Digital Library

[4]

Dorwin Cartwright and Frank Harary. 1956. Structural balance: A generalization of Heider’s theory.Psychological Review 63, 5 (1956), 277.

[5]

Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jaeho Choi. 2019. Rating augmentation with generative adversarial networks towards accurate collaborative filtering. In Proceedings of the World Wide Web Conference. 2616–2622.

Digital Library

[6]

Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018. CFGAN: A generic collaborative filtering framework based on generative adversarial networks. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 137–146.

Digital Library

[7]

Roshni Chakraborty, Srishti Bhandari, Nilotpal Chakraborty, and Ritwika Das. 2020. Eve2Sign: Creating signed networks of news events. In Proceedings of the 3rd Workshop on Narrative Extraction from Texts. 79–87.

[8]

Roshni Chakraborty, Maitry Bhavsar, Sourav Kumar Dandapat, and Joydeep Chandra. 2019. Tweet summarization of news articles: An objective ordering-based perspective. IEEE Transactions on Computational Social Systems 6, 4 (2019), 761–777.

[9]

Roshni Chakraborty, Maitry Bhavsar, Sourav Kumar Dandapat, and Joydeep Chandra. 2022. Detecting stance in tweets: A signed network based approach. arXiv:2201.07472. Retrieved from https://arxiv.org/abs/2201.07472.

[10]

Roshni Chakraborty and Nilotpal Chakraborty. 2020. Are we friends or enemies? Let’s ask thy neighbour! In Proceedings of the 25th International Conference on Intelligent User Interfaces Companion. 61–62.

Digital Library

[11]

Xiao Chen, Jing-Feng Guo, Xiao Pan, and Chunying Zhang. 2017. Link prediction in signed networks based on connection degree. Journal of Ambient Intelligence and Humanized Computing (2017), 1–11.

[12]

Xiao Chen, Jing-Feng Guo, Xiao Pan, and Chunying Zhang. 2019. Link prediction in signed networks based on connection degree. Journal of Ambient Intelligence and Humanized Computing 10, 5 (2019), 1747–1757.

[13]

Yiqi Chen, Tieyun Qian, Huan Liu, and Ke Sun. 2018. Bridge: Enhanced signed directed network embedding. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 773–782.

Digital Library

[14]

Kewei Cheng, Jundong Li, and Huan Liu. 2017. Unsupervised feature selection in signed social networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 777–786.

Digital Library

[15]

Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, and Ambuj Tewari. 2014. Prediction and clustering in signed networks: A local to global perspective. The Journal of Machine Learning Research 15, 1 (2014), 1177–1213.

Digital Library

[16]

Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, and Inderjit S. Dhillon. 2011. Exploiting longer cycles for link prediction in signed networks. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, 1157–1162.

Digital Library

[17]

Debasis Das. 2018. Positive and negative link prediction algorithm based on sentiment analysis in large social networks. Wireless Personal Communications 102, 3 (2018), 2183–2198.

Digital Library

[18]

Emily Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. Advances in Neural Information processing systems 28 (2015).

[19]

Tyler Derr, Yao Ma, and Jiliang Tang. 2018. Signed graph convolutional networks. In Proceedings of the 2018 IEEE International Conference on Data Mining. IEEE, 929–934.

[20]

Tyler Derr, Chenxing Wang, Suhang Wang, and Jiliang Tang. 2018. Relevance measurements in online signed social networks. In Proceedings of the 14th International Workshop on Mining and Learning with Graphs.

[21]

Patrick Doreian. 1989. Network autocorrelation models: Problems and prospects. In Spatial Statistics: Past, Present, Future. D. A. Griffith (Ed.), Institute of Mathematical Geography, 369–389.

[22]

Agata Fronczak, Piotr Fronczak, and Janusz A. Hołyst. 2004. Average path length in random networks. Physical Review E 70, 5 (Nov2004), 056110.

[23]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems. 2672–2680.

[24]

Ian J. Goodfellow. 2017. NIPS 2016 tutorial: Generative adversarial networks. arXiv:1701.00160. Retrieved from http://arxiv.org/abs/1701.00160.

[25]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 855–864.

Digital Library

[26]

Ramanthan Guha, Ravi Kumar, Prabhakar Raghavan, and Andrew Tomkins. 2004. Propagation of trust and distrust. In Proceedings of the 13th International Conference on World Wide Web. ACM, 403–412.

Digital Library

[27]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. Vol. 30.

[28]

Fritz Heider. 1958. The Psychology of Interpersonal Relations. Wiley, New York, NY.

[29]

Baofang Hu, Hong Wang, Xiaomei Yu, Weihua Yuan, and Tianwen He. 2017. Sparse network embedding for community detection and sign prediction in signed social networks. Journal of Ambient Intelligence and Humanized Computing 10, 1 (2017), 1–12.

[30]

Junjie Huang, Huawei Shen, Liang Hou, and Xueqi Cheng. 2019. Signed graph attention networks. In International Conference on Articial Neural Networks. Springer, 566–577.

[31]

Junjie Huang, Huawei Shen, Liang Hou, and Xueqi Cheng. 2021. SDGNN: Learning node representation for signed directed networks. In Proceedings of the AAAI Conference on Articial Intelligence, Vol. 35. 196–203.

[32]

Norman P. Hummon and Patrick Doreian. 2003. Some dynamics of social balance processes: Bringing Heider back into balance theory. Social Networks 25, 1 (2003), 17–49.

[33]

Mohammad Raihanul Islam, B. Aditya Prakash, and Naren Ramakrishnan. 2018. SIGNet: Scalable embeddings for signed networks. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 157–169.

Digital Library

[34]

Amin Javari, Tyler Derr, Pouya Esmailian, Jiliang Tang, and Kevin Chen-Chuan Chang. 2020. Rose: Role-based signed network embedding. In Proceedings of the Web Conference 2020. 2782–2788.

Digital Library

[35]

Amin Javari and Mahdi Jalili. 2014. Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Transactions on Intelligent Systems and Technology 5, 2 (2014), 24.

Digital Library

[36]

Amin Javari, HongXiang Qiu, Elham Barzegaran, Mahdi Jalili, and Kevin Chen-Chuan Chang. 2017. Statistical link label modeling for sign prediction: Smoothing sparsity by joining local and global information. In Proceedings of the 2017 IEEE International Conference on Data Mining. IEEE, 1039–1044.

[37]

Abtin Khodadadi and Mahdi Jalili. 2017. Sign prediction in social networks based on tendency rate of equivalent micro-structures. Neurocomputing 257 (2017), 175–184.

[38]

Junghwan Kim, Haekyu Park, Ji-Eun Lee, and U. Kang. 2018. Side: Representation learning in signed directed networks. In Proceedings of the 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee, 509–518.

Digital Library

[39]

Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. Retrieved from https://arxiv.org/abs/1609.02907.

[40]

Srijan Kumar, William L. Hamilton, Jure Leskovec, and Dan Jurafsky. 2018. Community interaction and conflict on the web. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 933–943.

Digital Library

[41]

Srijan Kumar, Francesca Spezzano, and V. S. Subrahmanian. 2014. Accurately detecting trolls in slashdot zoo via decluttering. In Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Press, 188–195.

Digital Library

[42]

Srijan Kumar, Francesca Spezzano, V. S. Subrahmanian, and Christos Faloutsos. 2016. Edge weight prediction in weighted signed networks. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining. IEEE, 221–230.

[43]

Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th International Conference on World Wide Web. ACM, 641–650.

Digital Library

[44]

Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Signed networks in social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1361–1370.

Digital Library

[45]

Yu Li, Yuan Tian, Jiawei Zhang, and Yi Chang. 2020. Learning signed network embedding via graph attention. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34, 4772–4779.

[46]

Wanyu Lin and Baochun Li. 2022. Status-aware signed heterogeneous network embedding with graph neural networks. IEEE Transactions on Neural Networks and Learning Systems (2022).

[47]

Chunyu Lu, Pengfei Jiao, Hongtao Liu, Yaping Wang, Hongyan Xu, and Wenjun Wang. 2019. SSNE: Status signed network embedding. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 81–93.

Digital Library

[48]

Can Lu, Jeffrey Xu Yu, Rong-Hua Li, and Hao Wei. 2016. Exploring hierarchies in online social networks. IEEE Transactions on Knowledge and Data Engineering 28, 8 (2016), 2086–2100.

Digital Library

[49]

Shuqi Lu, Zhicheng Dou, Xu Jun, Jian-Yun Nie, and Ji-Rong Wen. 2019. PSGAN: A minimax game for personalized search with limited and noisy click data. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 555–564.

Digital Library

[50]

Paolo Massa and Paolo Avesani. 2007. Trust-aware recommender systems. In Proceedings of the 2007 ACM Conference on Recommender Systems. ACM, 17–24.

Digital Library

[51]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems. 3111–3119.

Digital Library

[52]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 701– 710.

Digital Library

[53]

Amin Salehi and Hasan Davulcu. 2018. Detecting antagonistic and allied communities on social media. In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 99– 106.

[54]

Moshen Shahriari and Mahdi Jalili. 2014. Ranking nodes in signed social networks. Social Network Analysis and Mining 4, 1 (2014), 172.

[55]

Cosma Rohilla Shalizi and Andrew C. Thomas. 2011. Homophily and contagion are generically confounded in observational social network studies. Sociological Methods & Research 40, 2 (2011), 211–239.

[56]

Xiao Shen and Fu-Lai Chung. 2018. Deep network embedding for graph representation learning in signed networks. IEEE Transactions on Cybernetics 50, 4 (2018), 1556–1568.

[57]

Ranveer Singh and Bibhas Adhikari. 2017. Measuring the balance of signed networks and its application to sign prediction. Journal of Statistical Mechanics: Theory and Experiment 2017, 6 (2017), 063302.

[58]

Wenzhuo Song, Hongxu Chen, Xueyan Liu, Hongzhe Jiang, and Shengsheng Wang. 2021. Hyperbolic node embedding for signed networks. Neurocomputing 421 (2021), 329–339.

[59]

Jiliang Tang, Charu Aggarwal, and Huan Liu. 2016. Node classification in signed social networks. In Proceedings of the 2016 SIAM International Conference on Data Mining. SIAM, 54–62.

[60]

Jiliang Tang, Shiyu Chang, Charu Aggarwal, and Huan Liu. 2015. Negative link prediction in social media. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining. ACM, 87–96.

Digital Library

[61]

Jiliang Tang, Yi Chang, Charu Aggarwal, and Huan Liu. 2016. A survey of signed network mining in social media. ACM Computing Surveys 49, 3 (2016), 1–37.

Digital Library

[62]

Jiliang Tang, Xia Hu, and Huan Liu. 2014. Is distrust the negation of trust?: The value of distrust in social media. In Proceedings of the 25th ACM Conference on Hypertext and Social Media. ACM, New York, NY, 148–157.

Digital Library

[63]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. 1067–1077.

Digital Library

[64]

Fei Tian, Bin Gao, Qing Cui, Enhong Chen, and Tie-Yan Liu. 2014. Learning deep representations for graph clustering. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 28.

[65]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. Stat 1050 (2017), 20.

[66]

Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1225–1234.

Digital Library

[67]

Hongwei Wang, Jialin Wang, Jia Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Wenjie Li, Xing Xie, and Minyi Guo. 2019. Learning graph representation with generative adversarial nets. IEEE Transactions on Knowledge and Data Engineering 33, 8 (2019), 3090–3103.

[68]

Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. Graphgan: Graph representation learning with generative adversarial nets. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.

[69]

Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, and Qi Liu. 2018. Shine: Signed heterogeneous information network embedding for sentiment link prediction. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, 592–600.

Digital Library

[70]

Suhang Wang, Jiliang Tang, Charu Aggarwal, Yi Chang, and Huan Liu. 2017. Signed network embedding in social media. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, 327–335.

[71]

Robert West, Hristo S. Paskov, Jure Leskovec, and Christopher Potts. 2014. Exploiting social network structure for person-to-person sentiment analysis. Transactions of the Association for Computational Linguistics 2 (2014), 297–310.

[72]

Zhaoming Wu, Charu C. Aggarwal, and Jimeng Sun. 2016. The troll-trust model for ranking in signed networks. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 447–456.

Digital Library

[73]

Pinghua Xu, Wenbin Hu, Jia Wu, and Weiwei Liu. 2022. Signed network representation with novel node proximity evaluation. Neural Networks 148 (2022), 142–154.

Digital Library

[74]

Dengcheng Yan, Youwen Zhang, Wei Li, and Yiwen Zhang. 2021. MUSE: Multi-faceted attention for signed network embedding. arXiv:2104.14449. Retrieved from https://arxiv.org/abs/2104.14449.

[75]

Bo Yang, William Cheung, and Jiming Liu. 2007. Community mining from signed social networks. IEEE Transactions on Knowledge and Data Engineering 19, 10 (2007), 1333–1348.

Digital Library

[76]

Bo Yang, Xueyan Liu, Yang Li, and Xuehua Zhao. 2017. Stochastic blockmodeling and variational Bayes learning for signed network analysis. IEEE Transactions on Knowledge and Data Engineering 29, 9 (2017), 2026–2039.

Digital Library

[77]

Shuhan Yuan, Xintao Wu, and Yang Xiang. 2017. SNE: Signed network embedding. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 183–195.

[78]

Weiwei Yuan, Kangya He, Donghai Guan, and Guangjie Han. 2017. Edge-dual graph preserving sign prediction for signed social networks. IEEE Access 5 (2017), 19383–19392.

[79]

Weiwei Yuan, Chenliang Li, Guangjie Han, Donghai Guan, Li Zhou, and Kangya He. 2019. Negative sign prediction for signed social networks. Future Generation Computer Systems 93 (2019), 962–970.

Digital Library

[80]

Weinan Zhang. 2018. Generative adversarial nets for information retrieval: Fundamentals and advances. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 1375–1378.

Digital Library

Cited By

Anitha RVimal Kumar D(2024)Examining the Impact of User Credibility Score-Based Collaborative Filtering in E-commerce: Leveraging Machine Learning for Vehicle-to-Everything (V2X) Communication2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies10.1109/TQCEBT59414.2024.10545249(1-5)Online publication date: 22-Mar-2024
https://doi.org/10.1109/TQCEBT59414.2024.10545249
Lv YDuan JLi X(2024)A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networksComputer Science Review10.1016/j.cosrev.2024.10063552(100635)Online publication date: May-2024
https://doi.org/10.1016/j.cosrev.2024.100635
Sun HTian PXiong YZhang YXiang YJia XWang H(2024)DynamiSE: dynamic signed network embedding for link predictionMachine Language10.1007/s10994-023-06473-z113:7(4037-4053)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1007/s10994-023-06473-z

Index Terms

SigGAN: Adversarial Model for Learning Signed Relationships in Networks
1. Computer systems organization
  1. Embedded and cyber-physical systems

Recommendations

Link prediction on signed social networks based on latent space mapping

Link prediction is an essential research area in social network analysis. In recent years, link prediction in signed networks has drawn much concentration of the researchers. To predict potential positive and negative links, we should predict not only ...
Low rank modeling of signed networks
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Trust networks, where people leave trust and distrust feedback, are becoming increasingly common. These networks may be regarded as signed graphs, where a positive edge weight captures the degree of trust while a negative edge weight captures the degree ...
Exploiting Cluster-based Meta Paths for Link Prediction in Signed Networks
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Many online social networks can be described by signed networks, where positive links signify friendships, trust and like; while negative links indicate enmity, distrust and dislike. Predicting the sign of the links in these networks has attracted a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 1

January 2023

375 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3572846

Editor:
Charu Aggarwal
IBM T. J. Watson 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 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: 20 February 2023

Online AM: 20 May 2022

Accepted: 16 April 2022

Revised: 30 March 2022

Received: 29 March 2021

Published in TKDD Volume 17, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
398
Total Downloads

Downloads (Last 12 months)99
Downloads (Last 6 weeks)10

Reflects downloads up to 14 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Anitha RVimal Kumar D(2024)Examining the Impact of User Credibility Score-Based Collaborative Filtering in E-commerce: Leveraging Machine Learning for Vehicle-to-Everything (V2X) Communication2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies10.1109/TQCEBT59414.2024.10545249(1-5)Online publication date: 22-Mar-2024
https://doi.org/10.1109/TQCEBT59414.2024.10545249
Lv YDuan JLi X(2024)A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networksComputer Science Review10.1016/j.cosrev.2024.10063552(100635)Online publication date: May-2024
https://doi.org/10.1016/j.cosrev.2024.100635
Sun HTian PXiong YZhang YXiang YJia XWang H(2024)DynamiSE: dynamic signed network embedding for link predictionMachine Language10.1007/s10994-023-06473-z113:7(4037-4053)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1007/s10994-023-06473-z

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.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents