%0 Journal Article
%T Influence, originality and similarity in directed acyclic graphs
%A Stanislao Gualdi
%A Matus Medo
%A Yi-Cheng Zhang
%J Computer Science
%D 2011
%I arXiv
%R 10.1209/0295-5075/96/18004
%X 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.
%U http://arxiv.org/abs/1108.3691v1