{"type":"ScholarlyArticle","title":"Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks","authors":[{"name":"Moein Khajehnejad","slug":"moein-khajehnejad","given":"Moein","family":"Khajehnejad","affiliation":"Monash University"},{"name":"Ahmad Asgharian Rezaei","slug":"ahmad-asgharian-rezaei","given":"Ahmad","family":"Asgharian Rezaei","affiliation":"RMIT University"},{"name":"Mahmoudreza Babaei","slug":"mahmoudreza-babaei","given":"Mahmoudreza","family":"Babaei","affiliation":"Max Planck Institute for Software Systems"},{"name":"Jessica Hoffmann","slug":"jessica-hoffmann","given":"Jessica","family":"Hoffmann","affiliation":"The University of Texas at Austin"},{"name":"Mahdi Jalili","slug":"mahdi-jalili","given":"Mahdi","family":"Jalili","affiliation":"RMIT University"},{"name":"Adrian Weller","slug":"adrian-weller","given":"Adrian","family":"Weller","affiliation":"University of Cambridge; Alan Turing Institute"}],"abstract":"Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of individuals. It has critical applications in many fields, including viral marketing, information propagation, news dissemination, and vaccinations. However, the objective does not usually take into account whether the final set of influenced nodes is fair with respect to sensitive attributes, such as race or gender. Here we address fair influence maximization, aiming to reach minorities more equitably. We introduce Adversarial Graph Embeddings: we co-train an auto-encoder for graph embedding and a discriminator to discern sensitive attributes. This leads to embeddings which are similarly distributed across sensitive attributes. We then find a good initial set by clustering the embeddings. We believe we are the first to use embeddings for the task of fair influence maximization. While there are typically trade-offs between fairness and influence maximization objectives, our experiments on synthetic and real-world datasets show that our approach dramatically reduces disparity while remaining competitive with state-of-the-art influence maximization methods.","doi":"10.24963/ijcai.2020/594","url":"https://ijcai.org/proceedings/2020/594","pdf":"https://ijcai.org/proceedings/2020/0594.pdf","markdown":"https://ijcai.org/proceedings/2020/594.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/594","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":594,"pages":{"first":4306,"last":4312},"keywords":["AI Ethics: Fairness","Machine Learning Applications: Networks","Machine Learning: Adversarial Machine Learning","Natural Language Processing: Embeddings"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24130/file_24130.m3u8","length_sec":270,"provider":"hls"}],"references":[{"position":1,"raw":"[Adel et al., 2019] Tameem Adel, Isabel Valera, Zoubin Ghahra- mani, and Adrian Weller. One-network adversarial fairness. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 2412-2420, 2019.","title":"One-network adversarial fairness","authors":"Tameem Adel, Isabel Valera, Zoubin Ghahramani, Adrian Weller","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Aghaei et al., 2019] Sina Aghaei, Mohammad Javad Azizi, and Phebe Vayanos. Learning optimal and fair decision trees for non-discriminative decision-making. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 1418- 1426, 2019.","title":"Learning optimal and fair decision trees for non-discriminative decision-making","authors":"Aghaei","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ali et al., 2019] Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P. Gummadi, and Adish Singla. On the fairness of time-critical influence maxi- mization in social networks. ArXiv, abs/1905.06618, 2019.","title":"On the fairness of time-critical influence maximization in social networks","authors":"Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P Gummadi, Adish Singla","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Babaei et al., 2013] Mahmoudreza Babaei, Baharan Mirza- soleiman, Mahdi Jalili, and Mohammad Ali Safari. Revenue maximization in social networks through discounting. Social Network Analysis and Mining, 3(4):1249-1262, 2013.","title":"Mahmoudreza Babaei, Baharan Mirzasoleiman, Mahdi Jalili, and Mohammad Ali Safari. Revenue maximization in social networks through discounting","authors":"Babaei","year":"2013","venue":"Social Network Analysis and Mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Babaei et al., 2016] Mahmoudreza Babaei, Przemyslaw Grabow- icz, Isabel Valera, Krishna P Gummadi, and Manuel Gomez- Rodriguez. On the efficiency of the information networks in social media. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pages 83-92, 2016.","title":"On the efficiency of the information networks in social media","authors":"Babaei","year":"2016","venue":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Babaei et al., 2018] Mahmoudreza Babaei, Juhi Kulshrestha, Ab- hijnan Chakraborty, Fabrício Benevenuto, Krishna P Gummadi, and Adrian Weller. Purple feed: Identifying high consensus news posts on social media. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pages 10-16. ACM, 2018.","title":"Purple feed: Identifying high consensus news posts on social media","authors":"Babaei","year":"2018","venue":"Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Banerjee et al., 2013] Abhijit Banerjee, Arun G Chandrasekhar, Esther Duflo, and Matthew O Jackson. The diffusion of microfi- nance. Science, 341(6144), 2013.","title":"The diffusion of microfinance","authors":"Abhijit Banerjee, G Arun, Esther Chandrasekhar, Matthew O Duflo, Jackson","year":"2013","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Benabbou et al., 2018] Nawal Benabbou, Mithun Chakraborty, Xuan-Vinh Ho, Jakub Sliwinski, and Yair Zick. Diversity con- straints in public housing allocation. In AAMAS, pages 973-981, 2018.","title":"Diversity constraints in public housing allocation","authors":"Nawal Benabbou, Mithun Chakraborty, Xuan-Vinh Ho, Jakub Sliwinski, Yair Zick","year":"2018","venue":"AAMAS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cai et al., 2018] Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. A comprehensive survey of graph embed- ding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering, 30(9):1616-1637, 2018.","title":"A comprehensive survey of graph embedding: Problems, techniques, and applications","authors":"Hongyun Cai, Vincent W Zheng, Kevin Chen, -Chuan Chang","year":"2018","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Cao et al., 2015] Shaosheng Cao, Wei Lu, and Qiongkai Xu. Grarep: Learning graph representations with global structural in- formation. In Proceedings of the 24th ACM international on con- ference on information and knowledge management, pages 891- 900. ACM, 2015.","title":"Grarep: Learning graph representations with global structural information","authors":"Cao","year":"2015","venue":"Proceedings of the 24th ACM international on conference on information and knowledge management","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Carnes et al., 2007] Tim Carnes, Chandrashekhar Nagarajan, Ste- fan M Wild, and Anke Van Zuylen. Maximizing influence in a competitive social network: a follower's perspective. In EC, pages 351-360. ACM, 2007.","title":"Maximizing influence in a competitive social network: a follower's perspective","authors":"Tim Carnes, Chandrashekhar Nagarajan, Stefan M Wild, Anke Van Zuylen","year":"2007","venue":"EC","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fish et al., 2019] Benjamin Fish, Ashkan Bashardoust, danah boyd, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. Gaps in Information Access in Social Net- works. In WWW, 2019.","title":"Gaps in Information Access in Social Networks","authors":"Benjamin Fish, Ashkan Bashardoust, Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gomez-Rodriguez et al., 2010] M. Gomez-Rodriguez, J. Leskovec, and A. Krause. Inferring Networks of Diffu- sion and Influence. In KDD, 2010.","title":"Inferring Networks of Diffusion and Influence","authors":"M Gomez-Rodriguez, J Leskovec, A Krause","year":"2010","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672- 2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Goyal et al., 2013] Amit Goyal, Francesco Bonchi, Laks VS Lak- shmanan, and Suresh Venkatasubramanian. On minimizing bud- get and time in influence propagation over social networks. So- cial network analysis and mining, 3(2):179-192, 2013.","title":"On minimizing budget and time in influence propagation over social networks","authors":"Amit Goyal, Francesco Bonchi, Laks Vs Lakshmanan, Suresh Venkatasubramanian","year":"2013","venue":"Social network analysis and mining","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowl- edge discovery and data mining, pages 855-864. ACM, 2016.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Hamilton et al., 2017a] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, pages 1024-1034, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hamilton et al., 2017b] William L Hamilton, Rex Ying, and Jure Leskovec. Representation learning on graphs: Methods and ap- plications. arXiv preprint arXiv:1709.05584, 2017.","title":"Representation learning on graphs: Methods and applications","authors":"Hamilton","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Keikha et al., 2020] Mohammad Mehdi Keikha, Maseud Rah- gozar, Masoud Asadpour, and Mohammad Faghih Abdollahi. In- fluence maximization across heterogeneous interconnected net- works based on deep learning. Expert Systems with Applications, 140:112905, 2020.","title":"Influence maximization across heterogeneous interconnected networks based on deep learning","authors":"Mohammad Mehdi Keikha, Maseud Rahgozar, Masoud Asadpour, Mohammad Faghih Abdollahi","year":"2020","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kempe et al., 2003] David Kempe, Jon Kleinberg, and Éva Tardos. Maximizing the spread of influence through a social network. In KDD, 2003.","title":"Maximizing the spread of influence through a social network","authors":"David Kempe, Jon Kleinberg, Éva Tardos","year":"2003","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Khajehnejad, 2019] Moein Khajehnejad. Simnet: Similarity-based network embeddings with mean commute time. PloS one, 14(8):e0221172, 2019.","title":"Simnet: Similarity-based network embeddings with mean commute time","authors":"Moein Khajehnejad, Khajehnejad","year":"2019","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Kourtellis et al., 2013] Nicolas Kourtellis, Tharaka Alahakoon, Ramanuja Simha, Adriana Iamnitchi, and Rahul Tripathi. Identi- fying high betweenness centrality nodes in large social networks. Social Network Analysis and Mining, 3(4):899-914, 2013.","title":"Identifying high betweenness centrality nodes in large social networks","authors":"Nicolas Kourtellis, Tharaka Alahakoon, Ramanuja Simha, Adriana Iamnitchi, Rahul Tripathi","year":"2013","venue":"Social Network Analysis and Mining","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Leskovec et al., 2007] Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, and Natalie Glance. Cost-effective outbreak detection in networks. In KDD, pages 420-429. ACM, 2007.","title":"Cost-effective outbreak detection in networks","authors":"Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne Vanbriesen, Natalie Glance","year":"2007","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Li et al., 2018] Yuchen Li, Ju Fan, Yanhao Wang, and Kian-Lee Tan. Influence maximization on social graphs: A survey. TKDE, 30(10):1852-1872, 2018.","title":"Influence maximization on social graphs: A survey","authors":"Yuchen Li, Ju Fan, Yanhao Wang, Kian-Lee Tan","year":"2018","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Madras et al., 2018] David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. Learning adversarially fair and trans- ferable representations. In International Conference on Machine Learning, pages 3384-3393, 2018.","title":"Learning adversarially fair and transferable representations","authors":"David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel","year":"2018","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Mirzasoleiman et al., 2012] Baharan Mirzasoleiman, Mah- moudreza Babaei, and Mahdi Jalili. Immunizing complex networks with limited budget. EPL (Europhysics Letters), 98(3):38004, 2012.","title":"Baharan Mirzasoleiman, Mahmoudreza Babaei, and Mahdi Jalili. Immunizing complex networks with limited budget","authors":"Mirzasoleiman","year":"2012","venue":"Europhysics Letters)","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Mislove et al., 2010] Alan Mislove, Bimal Viswanath, Krishna P Gummadi, and Peter Druschel. You are who you know: inferring user profiles in online social networks. In WSDM, pages 251- 260. ACM, 2010.","title":"You are who you know: inferring user profiles in online social networks","authors":"Alan Mislove, Bimal Viswanath, Krishna P Gummadi, Peter Druschel","year":"2010","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Richardson and Domingos, 2002] Matthew Richardson and Pedro Domingos. Mining knowledge-sharing sites for viral marketing. In KDD, 2002.","title":"Matthew Richardson and Pedro Domingos. Mining knowledge-sharing sites for viral marketing","authors":"Domingos Richardson","year":"2002","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web, pages 1067-1077. International World Wide Web Conferences Steering Committee, 2015.","title":"Line: Large-scale information network embedding","authors":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei","year":"2015","venue":"Proceedings of the 24th international conference on world wide web","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Tsang et al., 2019] Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, and Yair Zick. Group-Fairness in Influence Maximiza- tion. arXiv preprint arXiv:1903.00967, 2019.","title":"Group-Fairness in Influence Maximization","authors":"Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick","year":"2019","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2019/831"},{"position":31,"raw":"[Wang et al., 2016] Daixin Wang, Peng Cui, and Wenwu Zhu. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1225-1234. ACM, 2016.","title":"Structural deep network embedding","authors":"Wang","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ye et al., 2012] Mao Ye, Xingjie Liu, and Wang-Chien Lee. Ex- ploring social influence for recommendation: a generative model approach. In SIGIR, pages 671-680. ACM, 2012.","title":"Exploring social influence for recommendation: a generative model approach","authors":"Mao Ye, Xingjie Liu, Wang-Chien Lee","year":"2012","venue":"SIGIR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}