Fairness of Information Flow in Social Networks

Zeinab S. Jalali, Qilan Chen, Shwetha M. Srikanta, Weixiang Wang, Myunghwan Kim, Hema Raghavan, Sucheta Soundarajan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Social networks form a major parts of people's lives, and individuals often make important life decisions based on information that spreads through these networks. For this reason, it is important to know whether individuals from different protected groups have equal access to information flowing through a network. In this article, we define the Information Unfairness (IUF) metric, which quantifies inequality in access to information across protected groups. We then introduce MinIUF, an algorithm for reducing inequalities in information flow by adding edges to the network. Finally, we provide an in-depth analysis of information flow with respect to an attribute of interest, such as gender, across different types of networks to evaluate whether the structure of these networks allows groups to equally access information flowing in the network. Moreover, we investigate the causes of unfairness in such networks and how it can be improved.

Original languageEnglish (US)
Article number79
JournalACM Transactions on Knowledge Discovery from Data
Issue number6
StatePublished - Feb 28 2023


  • Social Network Analysis
  • information fairness
  • information flow

ASJC Scopus subject areas

  • General Computer Science

Cite this