Accurate relational reasoning in edge-labeled graphs by multi-labeled random walk with restart

Jinhong Jung, Woojeong Jin, Ha-Myung Park, U Kang

Abstract: Given an edge-labeled graph and two nodes, how can we accurately infer the relation between the nodes? Reasoning how the nodes are related is a fundamental task in analyzing network data, and various relevance measures have been suggested to effectively identify relevance between nodes in graphs. Although many random walk based models have been extensively utilized to reveal relevance between nodes, they cannot distinguish how those nodes are related in terms of edge labels since the traditional surfer does not consider edge labels for estimating relevance scores. In this paper, we propose MuRWR (Multi-Labeled Random Walk with Restart), a novel random walk based model that accurately identifies how nodes are related with, considering multiple edge labels. We introduce a labeled random surfer whose label indicates the relation between starting and visiting nodes, and change the surfer’s label during random walks for multi-hop relational reasoning. We also learn appropriate rules on changing the surfer’s label from the edge-labeled graph to accurately infer relations. We develop an iterative algorithm for computing MuRWR, and prove the convergence guarantee of the algorithm. Through extensive experiments, we show that our model MuRWR provides the best inference performance.