Graph Neural Networks for Friend Ranking in Large-scale Social Platforms
Aravind Sankar, Yozen Liu, Jun Yu, Neil Shah
Abstract
Graph Neural Networks (GNNs) have recently enabled substantial advances in graph learning. Despite their rich representational capacity, GNNs remain under-explored for large-scale social modeling applications. One such industrially ubiquitous application is friend suggestion: recommending users other candidate users to befriend, to improve user connectivity, retention and engagement. However, modeling such user-user interactions on large-scale social platforms poses unique challenges: such graphs often have heavy-tailed degree distributions, where a significant fraction of users are inactive and have limited structural and engagement information. Moreover, users interact with different functionalities, communicate with diverse groups, and have multifaceted interaction patterns.