A better strategy of discovering link-pattern based communities by classical clustering methods

Chen Yi Lin, Jia Ling Koh, Arbee L.P. Chen

研究成果: 書貢獻/報告類型會議論文篇章

5 引文 斯高帕斯(Scopus)

摘要

The definition of a community in social networks varies with applications. To generalize different types of communities, the concept of linkpattern based community was proposed in a previous study to group nodes into communities, where the nodes in a community have similar intra-community and inter-community interaction behaviors. In this paper, by defining centroid of a community, a distance function is provided to measure the similarity between the link pattern of a node and the centroid of a community. The problem of discovering link-pattern based communities is transformed into a data clustering problem on nodes for minimizing a given objective function. By extending the partitioning methods of cluster analysis, two algorithms named G-LPC and KM-LPC are proposed to solve the problem. The experiment results show that KM-LPC outperforms the previous work on the efficiency, the memory utilization, and the clustering result. Besides, G-LPC achieves the best result approaching the optimal solution.

原文英語
主出版物標題Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
頁面56-67
頁數12
版本PART 1
DOIs
出版狀態已發佈 - 2010 十二月 1
事件14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, 印度
持續時間: 2010 六月 212010 六月 24

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
號碼PART 1
6118 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

其他

其他14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
國家/地區印度
城市Hyderabad
期間2010/06/212010/06/24

ASJC Scopus subject areas

  • 理論電腦科學
  • 電腦科學(全部)

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