Influence, originality and similarity in directed acyclic graphs

被引：14

作者：

Gualdi, S. ^{[1
]}

Medo, M. ^{[1
]}

Zhang, Y. -C. ^{[1
,2
]}

机构：

[1] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland

[2] Univ Elect Sci & Technol China, Web Sci Ctr, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China

来源：

EPL | 2011年 / 96卷 / 01期

基金：

瑞士国家科学基金会; 中国国家自然科学基金;

关键词：

COMPLEX NETWORKS; RECOMMENDER SYSTEMS; LINK-PREDICTION; MODELS;

D O I：

10.1209/0295-5075/96/18004

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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. Copyright (C) EPLA, 2011

引用

页数：6

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