Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks
CoRR(2024)
摘要
In this paper, we investigate the conditions under which link analysis
algorithms prevent minority groups from reaching high ranking slots. We find
that the most common link-based algorithms using centrality metrics, such as
PageRank and HITS, can reproduce and even amplify bias against minority groups
in networks. Yet, their behavior differs: one one hand, we empirically show
that PageRank mirrors the degree distribution for most of the ranking positions
and it can equalize representation of minorities among the top ranked nodes; on
the other hand, we find that HITS amplifies pre-existing bias in homophilic
networks through a novel theoretical analysis, supported by empirical results.
We find the root cause of bias amplification in HITS to be the level of
homophily present in the network, modeled through an evolving network model
with two communities. We illustrate our theoretical analysis on both synthetic
and real datasets and we present directions for future work.
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