Adversarial Graph Embeddings for fair influence maximization over social networks

Moein Khajehnejad, Ahmad Asgharian Rezaei, Mahmoudreza Babaei, Jessica Hoffmann, Mahdi Jalili, Adrian Weller

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

18 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
EditorsChristian Bessiere
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages4306-4312
Number of pages7
ISBN (Electronic)9780999241165
DOIs
Publication statusPublished - 2020
EventInternational Joint Conference on Artificial Intelligence-Pacific Rim International Conference on Artificial Intelligence 2020 - Yokohama, Japan
Duration: 7 Jan 202115 Jan 2021
Conference number: 29th/17th
https://www.ijcai.org/Proceedings/2020/ (Proceedings)
https://ijcai20.org (Website)

Conference

ConferenceInternational Joint Conference on Artificial Intelligence-Pacific Rim International Conference on Artificial Intelligence 2020
Abbreviated titleIJCAI-PRICAI 2020
Country/TerritoryJapan
CityYokohama
Period7/01/2115/01/21
OtherIJCAI-PRICAI 2020, the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence!IJCAI-PRICAI2020 will take place January 7-15, 2021 online in a virtual reality in Japanese Standard Time (JST) zone.
Internet address

Keywords

  • AI Ethics
  • Fairness
  • Machine Learning
  • Adversarial Machine Learning
  • Natural Language Processing
  • Embeddings
  • Machine Learning Applications
  • Networks

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