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
收藏
页码：1819 / 1830
页数：12
相关论文
共 52 条
[1]  
Acar E., 2009, IEEE INT C DAT MIN W
[2]  
Agrawal R., 2003, P 12 INT WORLD WID W
[3]  
[Anonymous], P 8 INT WORLD WID WE
[4]  
[Anonymous], 1988, Algorithms for Clustering Data
[5]  
[Anonymous], 2005, P 11 ACM SIGKDD INT
[6]  
Asur S., 2007, P 13 ACM INT SIGKDD
[7]  
Bird C., 2006, P 2006 INT WORKSH MI
[8]  
Boaventura P. O., 1996, GRAFOS TEORIA MODELO
[9]  
Cai D., 2005, P 2005 EUR C PRINC P
[10]  
Carpenter T., 2002, PRACTICAL ISSUES ALG
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