收稿日期: 2016-05-16
修回日期: 2016-08-26
网络出版日期: 2016-10-20
基金资助
本文系国家高技术研究发展计划（“863”计划）“微生物数字资源知识管理系统构建及关键技术研究”（项目编号：2014AA021503）和中国科学院2013年度“西部之光”人才培养计划“引文耦合网络演化分析及在科技评价与预测中的应用研究”（项目编号：科发人字165号（3-6））研究成果之一。
Community Detection Algorithm Based on Sample Weighting
Received date: 2016-05-16
Revised date: 2016-08-26
Online published: 2016-10-20
肖雪 , 王钊伟 , 陈云伟 , 邓勇 . 基于样本加权的引文网络的社团划分[J]. 图书情报工作, 2016 , 60(20) : 86 -93 . DOI: 10.13266/j.issn.0252-3116.2016.20.011
[Purpose/significance] The study of community discovery has great value for text mining. In order to improve the accuracy of the communities of citation networks, this paper describes a new community discovering algorithm for literature based on weighted networks. [Method/process] The algorithm was based on the "Louvain community detecting algorithm", and established the vector space model to calculate the similarity of the adjacent papers as the weight of the link. Finally, based on the weighted network, the authors detected the community structure of the network. [Result/conclusion] Experiments show that the proposed algorithm is an effective solution to improve the performance of community detection.
Key words： citation network; community discovery; clustering; text mining
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