摘要
针对团伙犯罪的发案率不断升高,且其造成的社会危害性较大的社会现状,根据执法机构的数据分析需求,文章引入社会网络分析的相关理论与方法,对海量交易进行数学建模。采用图聚类算法挖掘出交易团伙,解析其内部结构,智能识别出从事非法交易的犯罪团伙,从而为执法机构提供准确的犯罪线索,增强打击刑事犯罪的主动性及效率。
Social network theory is used to mathematically model massive transactions for aiding law enforcement agencies, in the status of art of increasing rate of gang crimes which are more dangerous than common crimes. Graph-based clustering algorithms are employed to mine out gangs of transactions, to analyze their structures and smartly identify gangs engaged in illegal transactions. Thereby, it provides for accurate crime clues for law enforcement agencies, and enhances initiative and efifciency to crack down on criminal offenses.
出处
《信息网络安全》
2014年第6期88-91,共4页
Netinfo Security
关键词
社会网络分析
数学建模
图聚类算法
犯罪团伙
social network analysis
mathematical modeling
graph-based clustering algorithm
criminal gang
