Potential theory for directed networks
- Zhang, Qian-Ming Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Lü, Linyuan Institute of Information Economy, Alibaba Business College, Hangzhou Normal University, China - Department of Physics, University of Fribourg, Switzerland
- Wang, Wen-Qiang Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Zhu, Yu-Xiao Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Zhou, Tao Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
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11.02.2013
Published in:
- PLoS ONE. - 2013, vol. 8, no. 2, p. e55437
English
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.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 31961
- DOI 10.1371/journal.pone.0055437
- Persistent URL
- https://folia.unifr.ch/unifr/documents/303009
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