A Recursive Bayesian Approach for the Link Prediction Problem

Abstract

Recently, link prediction techniques have been increasingly adopted to discover link patterns in various domains. On challenging problem is to improve the performance continually. In this paper, we propose a recursive prediction mechanism to addresses the link prediction problem. A posterior is calculated based on observed data, and then we estimate the state of the graph and use the posterior as the prior distribution for the next stage. With the increasing of iterations, the proposed approach incorporates more and more topological structure information and node attributes data. Experimental results with real-world networks have shown that the proposed solution performs better in terms of well-known metrics as compared to the existing approaches. This novel approach has already been integrated into an expert system and provides auxiliary support for decision-makers.