Potential theory for directed networks

Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential t...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2013-02, Vol.8 (2), p.e55437-e55437
Main Authors: Zhang, Qian-Ming, Lü, Linyuan, Wang, Wen-Qiang, Zhu, Yu-Xiao, Zhou, Tao
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Clustering
Computer Science
Data mining
Data processing
Ecosystems
Engineering
Environmental science
Experiments
Friendship
Hyperlinks
Information Storage and Retrieval
International conferences
Laboratories
Models, Theoretical
Network analysis
Network formation
Networks
Physics
Potential theory
Semantic web
Semantics
Social and Behavioral Sciences
Social networks
World Wide Web
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds to a decrease of a unit potential and subgraphs with definable potential values for all nodes are preferred. Combining the potential theory with the clustering and homophily mechanisms, it is deduced that the Bi-fan structure consisting of 4 nodes and 4 directed links is the most favored local structure in directed networks. Our hypothesis receives strongly positive supports from extensive experiments on 15 directed networks drawn from disparate fields, as indicated by the most accurate and robust performance of Bi-fan predictor within the link prediction framework. In summary, our main contribution is twofold: (i) We propose a new mechanism for the local organization of directed networks; (ii) We design the corresponding link prediction algorithm, which can not only testify our hypothesis, but also find out direct applications in missing link prediction and friendship recommendation.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0055437