Link recommendation algorithms contribute to shaping human relations of
billions of users worldwide in social networks. To maximize relevance, they
typically propose connecting users that are similar to each other. This has
been found to create information silos, exacerbating the isolation suffered by
vulnerable salient groups and perpetuating societal stereotypes. To mitigate
these limitations, a significant body of work has been devoted to the
implementation of fair link recommendation methods. However, most approaches do
not question the ultimate goal of link recommendation algorithms, namely the
monetization of users' engagement in intricate business models of data trade.
This paper advocates for a diversification of players and purposes of social
network platforms, aligned with the pursue of social justice. To illustrate
this conceptual goal, we present ERA-Link, a novel link recommendation
algorithm based on spectral graph theory that counteracts the systemic societal
discrimination suffered by vulnerable groups by explicitly implementing
affirmative action. We propose four principled evaluation measures, derived
from effective resistance, to quantitatively analyze the behavior of the
proposed method and compare it to three alternative approaches. Experiments
with synthetic and real-world networks illustrate how ERA-Link generates better
outcomes according to all evaluation measures, not only for the vulnerable
group but for the whole network. In other words, ERA-Link recommends
connections that mitigate the structural discrimination of a vulnerable group,
improves social cohesion and increases the social capital of all network users.
Furthermore, by promoting the access to a diversity of users, ERA-Link
facilitates innovation opportunities.

本文倡导实现社会公正，提出 ERA-Link 算法，利用谱图理论反对不公，并通过四个评估指标的实验，证明 ERA-Link 能够在不仅仅是弱势群体的情况下改善社交网络结构，并促进整体社交资本的增加和创新机会的提升。