A random graph generation algorithm for the analysis of social networks

被引:5
|
作者
Morris, James F. [1 ]
O'Neal, Jerome W. [1 ]
Deckro, Richard F. [1 ]
机构
[1] Wright Patterson AFB, Air Force Inst Technol, Dept Operat Sci, Future Operat Invest Lab, 4180 Watson Way, Dayton, OH 45433 USA
来源
JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS | 2014年 / 11卷 / 03期
关键词
social network analysis; random graph generation; connected graphs; assortative mixing; clustering;
D O I
10.1177/1548512912450370
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Social network analysis (SNA) is a rapidly growing field with numerous applications in industry and government. However, the field still lacks means to generate random social networks with certain desired properties, thus inhibiting their ability to test new SNA algorithms and metrics. Available random graph generation algorithms suffer from tendencies to generate disconnected graphs and sometimes induce undesirable network properties. In this paper, we present an algorithm, the prescribed node degree, connected graph (PNDCG) algorithm, designed to generate weakly connected social networks. Extensions to the PNDCG algorithm allow one to create random graphs that control the clustering coefficient and degree correlation within the generated networks. Empirical test results demonstrate the capability of the PNDCG algorithm to produce networks with the desired properties.
引用
收藏
页码:265 / 276
页数:12
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