On the information unfairness of social networks

Zeinab S. Jalali, Weixiang Wang, Myunghwan Kim, Hema Raghavan, Sucheta Soundarajan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social networks play a vital role in the spread of information through a population, and individuals in networks make important life decisions on the basis of the information to which they have access. In many cases, it is important to evaluate whether information is spreading fairly to all groups in a network. For instance, are male and female students equally likely to hear about a new scholarship? In this paper, we present the information unfairness criterion, which measures whether information spreads fairly to all groups in a network. We perform a thorough case study on the DBLP computer science co-authorship network with respect to gender. We then propose MaxFair, an algorithm to add edges to a network to decrease information unfairness, and evaluate on several real-world network datasets.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
EditorsCarlotta Demeniconi, Nitesh Chawla
PublisherSociety for Industrial and Applied Mathematics Publications
Pages613-621
Number of pages9
ISBN (Electronic)9781611976236
DOIs
StatePublished - 2020
Event2020 SIAM International Conference on Data Mining, SDM 2020 - Cincinnati, United States
Duration: May 7 2020May 9 2020

Publication series

NameProceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020

Conference

Conference2020 SIAM International Conference on Data Mining, SDM 2020
CountryUnited States
CityCincinnati
Period5/7/205/9/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Fingerprint Dive into the research topics of 'On the information unfairness of social networks'. Together they form a unique fingerprint.

Cite this