{"type":"ScholarlyArticle","title":"Fairwalk: Towards Fair Graph Embedding","authors":[{"name":"Tahleen Rahman","slug":"tahleen-rahman","given":"Tahleen","family":"Rahman","affiliation":"CISPA Helmholtz Center for Information Security"},{"name":"Bartlomiej Surma","slug":"bartlomiej-surma","given":"Bartlomiej","family":"Surma","affiliation":"CISPA Helmholtz Center for Information Security"},{"name":"Michael Backes","slug":"michael-backes","given":"Michael","family":"Backes","affiliation":"CISPA Helmholtz Center for Information Security"},{"name":"Yang Zhang","slug":"yang-zhang","given":"Yang","family":"Zhang","affiliation":"CISPA Helmholtz Center for Information Security"}],"abstract":"Graph embeddings have gained huge popularity in the recent years as a powerful tool to analyze social networks. However, no prior works have studied potential bias issues inherent within graph embedding. In this paper, we make a first attempt in this direction. In particular, we concentrate on the fairness of node2vec, a popular graph embedding method. Our analyses on two real-world datasets demonstrate the existence of bias in node2vec when used for friendship recommendation. We, therefore, propose a fairness-aware embedding method, namely Fairwalk, which extends node2vec. Experimental results demonstrate that Fairwalk reduces bias under multiple fairness metrics while still preserving the utility.","doi":"10.24963/ijcai.2019/456","url":"https://ijcai.org/proceedings/2019/456","pdf":"https://ijcai.org/proceedings/2019/0456.pdf","markdown":"https://ijcai.org/proceedings/2019/456.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/456","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":456,"pages":{"first":3289,"last":3295},"keywords":["Machine Learning Applications: Networks","Machine Learning: Data Mining","Multidisciplinary Topics and Applications: Social Sciences"],"videos":[],"references":[{"position":1,"raw":"[Backes et al., 2017] Michael Backes, Mathias Humbert, Jun Pang, and Yang Zhang. walk2friends: Inferring So- cial Links from Mobility Profiles. In Proc. CCS, pages 1943-1957, 2017.","title":"walk2friends: Inferring Social Links from Mobility Profiles","authors":"Michael Backes, Mathias Humbert, Jun Pang, Yang Zhang","year":"2017","venue":"Proc. CCS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Barocas and Selbst, 2016] Solon Barocas and Andrew D Selbst. Big data's disparate impact. Cal. L. Rev., 104:671, 2016.","title":"Big data's disparate impact","authors":"Selbst Barocas, Solon Barocas, Andrew D Selbst","year":"2016","venue":"Cal. L. Rev","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Buolamwini and Gebru, 2018] Joy Buolamwini and Timnit Gebru. Gender Shades: Intersectional Accuracy Dispari- ties in Commercial Gender Classification. In Proc. FAT*, pages 77-91, 2018.","title":"Joy Buolamwini and Timnit Gebru. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification","authors":"Gebru Buolamwini","year":"2018","venue":"Proc. FAT*","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Calders and Verwer, 2010] Toon Calders and Sicco Ver- wer. Three naive bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21(2):277-292, 2010.","title":"Toon Calders and Sicco Verwer. Three naive bayes approaches for discrimination-free classification","authors":"Verwer Calders","year":"2010","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cho et al., 2011] Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and Mobility: User Movement in Location-based Social Networks. In Proc. KDD, pages 1082-1090, 2011.","title":"Friendship and Mobility: User Movement in Location-based Social Networks","authors":"Cho","year":"2011","venue":"Proc. KDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dwork et al., 2012] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness Through Awareness. In Proc. ITCS, pages 214-226, 2012.","title":"Fairness Through Awareness","authors":"Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel","year":"2012","venue":"Proc. ITCS","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Feldman et al., 2015] Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and Removing Disparate Impact. In Proc. KDD, pages 259-268, 2015.","title":"Certifying and Removing Disparate Impact","authors":"Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, Suresh Venkatasubramanian","year":"2015","venue":"Proc. KDD","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Flaxman et al., 2016] Seth Flaxman, Sharad Goel, and Justin M Rao. Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1):298-320, 2016.","title":"Filter bubbles, echo chambers, and online news consumption","authors":"Seth Flaxman, Sharad Goel, Justin M Rao","year":"2016","venue":"Public Opinion Quarterly","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable Feature Learning for Networks. In Proc. KDD, pages 855-864, 2016.","title":"Aditya Grover and Jure Leskovec. node2vec: Scalable Feature Learning for Networks","authors":"Leskovec Grover","year":"2016","venue":"Proc. KDD","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hardt et al., 2016] Moritz Hardt, Eric Price, and Nati Sre- bro. Equality of Opportunity in Supervised Learning. In Proc. NIPS, pages 3315-3323, 2016.","title":"Equality of Opportunity in Supervised Learning","authors":"Moritz Hardt, Eric Price, Nati Srebro","year":"2016","venue":"Proc. NIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hargreaves et al., 2019] Eduardo Hargreaves, Claudio Agosti, Daniel Menasché, Giovanni Neglia, Alexandre Reiffers-Masson, and Eitan Altman. Fairness in online social network timelines: Measurements, models and mechanism design. Performance Evaluation, 129:15-39, 2019.","title":"Fairness in online social network timelines: Measurements, models and mechanism design","authors":"Eduardo Hargreaves, Claudio Agosti, Daniel Menasché, Giovanni Neglia, Alexandre Reiffers-Masson, Eitan Altman","year":"2019","venue":"Performance Evaluation","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kamiran et al., 2010] Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy. Discrimination Aware Decision Tree Learning. In Proc. ICDM, pages 869-874, 2010.","title":"Discrimination Aware Decision Tree Learning","authors":"Faisal Kamiran, Toon Calders, Mykola Pechenizkiy","year":"2010","venue":"Proc. ICDM","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kamishima et al., 2012] Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. Fairness-Aware Classifier with Prejudice Remover Regularizer. In Proc. ECML/PKDD, pages 35-50, 2012.","title":"Fairness-Aware Classifier with Prejudice Remover Regularizer","authors":"Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma","year":"2012","venue":"Proc. ECML/PKDD","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Karimi et al., 2018] Fariba Karimi, Mathieu Génois, Clau- dia Wagner, Philipp Singer, and Markus Strohmaier. Ho- mophily Influences Ranking of Minorities in Social Net- works. Scientific Reports, 2018.","title":"Homophily Influences Ranking of Minorities in Social Networks","authors":"Fariba Karimi, Mathieu Génois, Claudia Wagner, Philipp Singer, Markus Strohmaier","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kearns et al., 2019] Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. An Empirical Study of Rich Subgroup Fairness for Machine Learning. In Proc. FAT*, pages 100-109, 2019.","title":"An Empirical Study of Rich Subgroup Fairness for Machine Learning","authors":"Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu","year":"2019","venue":"Proc. FAT*","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kleinberg et al., 2017] Jon Kleinberg, Sendhil Mul- lainathan, and Manish Raghavan. Inherent trade-offs in the fair determination of risk scores. In LIPIcs, volume 67. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017.","title":"Inherent trade-offs in the fair determination of risk scores","authors":"Kleinberg","year":"2017","venue":"LIPIcs","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Kai Chen, Greg Cor- rado, and Jeffrey Dean. Efficient Estimation of Word Rep- resentations in Vector Space. In Proc. ICLR, 2013.","title":"Efficient Estimation of Word Representations in Vector Space","authors":"Mikolov","year":"2013","venue":"Proc. ICLR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. DeepWalk: Online Learning of Social Rep- resentations. In Proc. KDD, pages 701-710, 2014.","title":"DeepWalk: Online Learning of Social Representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proc. KDD","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Quattrociocchi et al., 2016] Walter Quattrociocchi, Antonio Scala, and Cass R Sunstein. Echo chambers on facebook. Available at SSRN 2795110, 2016.","title":"Echo chambers on facebook","authors":"Walter Quattrociocchi, Antonio Scala, Cass R Sunstein","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Romei and Ruggieri, 2014] Andrea Romei and Salvatore Ruggieri. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review, 29(5):582- 638, 2014.","title":"A multidisciplinary survey on discrimination analysis","authors":"Andrea Romei, Salvatore Ruggieri","year":"2014","venue":"The Knowledge Engineering Review","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Stoica et al., 2018] Ana-Andreea Stoica, Christopher Riederer, and Augustin Chaintreau. Algorithmic Glass Ceiling in Social Networks: The effects of social recom- mendations on network diversity. In Proc. WWW, pages 923-932, 2018.","title":"Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity","authors":"Ana-Andreea Stoica, Christopher Riederer, Augustin Chaintreau","year":"2018","venue":"Proc. WWW","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tang et al., 2015a] Jian Tang, Meng Qu, and Qiaozhu Mei. PTE: Predictive Text Embedding through Large-scale Het- erogeneous Text Networks. In Proc. KDD, pages 1165- 1174, 2015.","title":"PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks","authors":"Tang","year":"2015","venue":"Proc. KDD","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tang et al., 2015b] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. LINE: Large- scale Information Network Embedding. In Proc. WWW, pages 1067-1077, 2015.","title":"LINE: Largescale Information Network Embedding","authors":"Tang","year":"2015","venue":"Proc. WWW","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yang and Stoyanovich, 2017] Ke Yang and Julia Stoy- anovich. Measuring fairness in ranked outputs. In SSDBM, 2017.","title":"Measuring fairness in ranked outputs","authors":"Ke Yang, Julia Stoyanovich","year":"2017","venue":"SSDBM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zafar et al., 2017] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi. Fair- ness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. In Proc. WWW, pages 1171-1180, 2017.","title":"Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment","authors":"Zafar","year":"2017","venue":"Proc. WWW","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zehlike et al., 2017] Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ri- cardo Baeza-Yates. FA*IR: A Fair Top-k Ranking Algo- rithm. In Proc. CIKM, pages 1569-1578, 2017.","title":"FA*IR: A Fair Top-k Ranking Algorithm","authors":"Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates","year":"2017","venue":"Proc. CIKM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}