TY - GEN
T1 - A vertex similarity probability model for finding network community structure
AU - Li, Kan
AU - Pang, Yin
PY - 2012
Y1 - 2012
N2 - Most methods for finding community structure are based on the prior knowledge of network structure type. These methods grouped the communities only when known network is unipartite or bipartite. This paper presents a vertex similarity probability (VSP) model which can find community structure without priori knowledge of network structure type. Vertex similarity, which assumes that, for any type of network structures, vertices in the same community have similar properties. In the VSP model, "Common neighbor index" is used to measure the vertex similarity probability, as it has been proved to be an effective index for vertex similarity. We apply the algorithm to real-world network data. The results show that the VSP model is uniform for both unipartite networks and bipartite networks, and it is able to find the community structure successfully without the use of the network structure type.
AB - Most methods for finding community structure are based on the prior knowledge of network structure type. These methods grouped the communities only when known network is unipartite or bipartite. This paper presents a vertex similarity probability (VSP) model which can find community structure without priori knowledge of network structure type. Vertex similarity, which assumes that, for any type of network structures, vertices in the same community have similar properties. In the VSP model, "Common neighbor index" is used to measure the vertex similarity probability, as it has been proved to be an effective index for vertex similarity. We apply the algorithm to real-world network data. The results show that the VSP model is uniform for both unipartite networks and bipartite networks, and it is able to find the community structure successfully without the use of the network structure type.
KW - common neighbor index
KW - community structure
KW - type of the network structure
KW - vertex similarity
UR - http://www.scopus.com/inward/record.url?scp=84861442811&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-30217-6_38
DO - 10.1007/978-3-642-30217-6_38
M3 - Conference contribution
AN - SCOPUS:84861442811
SN - 9783642302169
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 456
EP - 467
BT - Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
T2 - 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
Y2 - 29 May 2012 through 1 June 2012
ER -