Link prediction in complex networks based on an information allocation index

被引:18
|
作者
Pei, Panpan [1 ]
Liu, Bo [1 ]
Jiao, Licheng [1 ]
机构
[1] Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Key Lab Intelligent Percept & Image Understanding, Minist Educ,Int Res Ctr Intelligent Percept & Com, Xian 710071, Shaanxi Provinc, Peoples R China
基金
中国国家自然科学基金;
关键词
Complex networks; Link prediction; Information theory; Resource allocation; BLOCKMODELS;
D O I
10.1016/j.physa.2016.11.069
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
An important issue in link prediction of complex networks is to make full use of different kinds of available information simultaneously. To tackle this issue, recently, an information-theoretic model has been proposed and a novel Neighbor Set Information Index (NSI) has been designed. Motivated by this work, we proposed a more general information-theoretic model by further distinguishing the contributions from different variables of the available features. Then, by introducing the resource allocation process into the model, we designed a new index based on neighbor sets with a virtual information allocation process: Neighbor Set Information Allocation Index(NSIA). Experimental studies on real world networks from disparate fields indicate that NSIA performs well compared with NSI as well as other typical proximity indices. (C) 2016 Elsevier B.V. All rights reserved.
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页码:1 / 11
页数:11
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