Link prediction by exploiting network formation games in exchangeable graphs

摘要

In social network analysis, we often need to predict new links, given some available evidence. This may, for instance, enable us to study user behavior and infer likely new interactions in the near future. Recently, a family of algorithms based on exchangeable graphs has proven effective for link prediction. The network is modeled as an exchangeable array, whose entries can flexibly be traced back to random function priors (e.g., block models, Gaussian Processes). Unfortunately, the burdensome computational complexity of these methods inhibit their application to even just moderate-scale networks. In this paper, we present a novel online training algorithm based on local Gaussian processes on subgraphs, which successfully overcomes this challenge. Moreover, we address the sparsity problem of links in social networks by presenting an improved algorithm based on network formation games. The network formation games we design also shed light on the ambiguity of missing links - not observed vs. non-existing. We evaluate our method against state-of-the-art algorithms on real-world datasets, demonstrating both the effectiveness and the efficiency of our method.