TY - GEN
T1 - Community evolution in dynamic multi-mode networks
AU - Tang, Lei
AU - Liu, Huan
AU - Zhang, Jianping
AU - Nazeri, Zohreh
PY - 2008
Y1 - 2008
N2 - A multi-mode network typically consists of multiple heterogeneous social actors among which various types of interactions could occur. Identifying communities in a multi-mode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network and the membership of groups often evolve gradually. In a dynamic multi-mode network, both actor membership and interactions can evolve, which poses a challenging problem of identifying community evolution. In this work, we try to address this issue by employing the temporal information to analyze a multi-mode network. A spectral framework and its scalability issue are carefully studied. Experiments on both synthetic data and real-world large scale networks demonstrate the efficacy of our algorithm and suggest its generality in solving problems with complex relationships.
AB - A multi-mode network typically consists of multiple heterogeneous social actors among which various types of interactions could occur. Identifying communities in a multi-mode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network and the membership of groups often evolve gradually. In a dynamic multi-mode network, both actor membership and interactions can evolve, which poses a challenging problem of identifying community evolution. In this work, we try to address this issue by employing the temporal information to analyze a multi-mode network. A spectral framework and its scalability issue are carefully studied. Experiments on both synthetic data and real-world large scale networks demonstrate the efficacy of our algorithm and suggest its generality in solving problems with complex relationships.
KW - Community evolution
KW - Dynamic heterogeneous network
KW - Dynamic network analysis
KW - Evolution
KW - Multi-mode networks
UR - http://www.scopus.com/inward/record.url?scp=65449147147&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=65449147147&partnerID=8YFLogxK
U2 - 10.1145/1401890.1401972
DO - 10.1145/1401890.1401972
M3 - Conference contribution
AN - SCOPUS:65449147147
SN - 9781605581934
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 677
EP - 685
BT - KDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
T2 - 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
Y2 - 24 August 2008 through 27 August 2008
ER -