Influence, originality and similarity in directed acyclic graphs

¹ Physics Department, University of Fribourg - CH-1700 Fribourg, Switzerland
² Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China - Chengdu 610054, PRC

Received: 23 May 2011
Accepted: 12 August 2011

Abstract

We introduce a framework for network analysis based on random walks on directed acyclic graphs where the probability of passing through a given node is the key ingredient. We illustrate its use in evaluating the mutual influence of nodes and discovering seminal papers in a citation network. We further introduce a new similarity metric and test it in a simple personalized recommendation process. This metric's performance is comparable to that of classical similarity metrics, thus further supporting the validity of our framework.