Group and link analysis of multi-relational scientific social networks

被引：32

作者：

Stroeele, Victor ^{[1
]}

Zimbrao, Geraldo ^{[2
]}

Souza, Jano M. ^{[2
]}

机构：

[1] UFJF Fed Univ Juiz de Fora, Grad Sch Comp Sci, BR-36001970 Juiz De Fora, MG, Brazil

[2] UFRJ Fed Univ Rio de Janeiro, COPPE, Grad Sch Comp Sci, BR-21945970 Rio De Janeiro, RJ, Brazil

来源：

JOURNAL OF SYSTEMS AND SOFTWARE | 2013年 / 86卷 / 07期

关键词：

Multi-relational scientific social network analysis; Max-flow grouping algorithm; Link prediction/suggestion; KNOWLEDGE; PATTERNS; FLOW;

D O I：

10.1016/j.jss.2013.02.024

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Analyzing social networks enables us to detect several inter and intra connections between people in and outside their organizations. We model a multi-relational scientific social network where researchers may have four different types of relationships with each other. We adopt some criteria to enable the modeling of a scientific social network as close as possible to reality. Using clustering techniques with maximum flow measure, we identify the social structure and research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific community. Finally, we evaluate the temporal evolution of scientific social networks to suggest/predict new relationships. (C) 2013 Elsevier Inc. All rights reserved.

引用

页码：1819 / 1830

页数：12

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