{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T19:22:13Z","timestamp":1771960933559,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/456","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"3289-3295","source":"Crossref","is-referenced-by-count":101,"title":["Fairwalk: Towards Fair Graph Embedding"],"prefix":"10.24963","author":[{"given":"Tahleen","family":"Rahman","sequence":"first","affiliation":[{"name":"CISPA Helmholtz Center for Information Security"}]},{"given":"Bartlomiej","family":"Surma","sequence":"additional","affiliation":[{"name":"CISPA Helmholtz Center for Information Security"}]},{"given":"Michael","family":"Backes","sequence":"additional","affiliation":[{"name":"CISPA Helmholtz Center for Information Security"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"CISPA Helmholtz Center for Information Security"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:49:26Z","timestamp":1564285766000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/456"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/456","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}