Influence, originality and similarity in directed acyclic graphs
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
Accepted: 12 August 2011
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.
PACS: 89.75.Hc – Networks and genealogical trees / 07.05.Kf – Data analysis: algorithms and implementation; data management / 89.20.-a – Interdisciplinary applications of physics
© EPLA, 2011