Supervised Random Walks: Predicting and Recommending Links in Social Networks

TL;DR

This paper introduces Supervised Random Walks, a novel algorithm that combines network structure with node and edge attributes to improve link prediction accuracy in social networks.

Contribution

The paper presents a new supervised learning algorithm that guides random walks using node and edge attributes for better link prediction.

Findings

Outperforms state-of-the-art unsupervised methods

Effective on Facebook social graph data

Scales efficiently to large networks

Abstract

Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function. Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.