TY - GEN
T1 - A better strategy of discovering link-pattern based communities by classical clustering methods
AU - Lin, Chen Yi
AU - Koh, Jia Ling
AU - Chen, Arbee L.P.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Clustering algorithms
KW - Link-pattern based community
KW - Social network
UR - http://www.scopus.com/inward/record.url?scp=79956292361&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79956292361&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13657-3_9
DO - 10.1007/978-3-642-13657-3_9
M3 - Conference contribution
AN - SCOPUS:79956292361
SN - 3642136567
SN - 9783642136566
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 56
EP - 67
BT - Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
T2 - 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
Y2 - 21 June 2010 through 24 June 2010
ER -