TY - GEN
T1 - Evolutionary clustering and analysis of bibliographic networks
AU - Gupta, Manish
AU - Aggarwal, Charu C.
AU - Han, Jiawei
AU - Sun, Yizhou
PY - 2011
Y1 - 2011
N2 - In this paper, we study the problem of evolutionary clustering of multi-typed objects in a heterogeneous bibliographic network. The traditional methods of homogeneous clustering methods do not result in a good typed-clustering. The design of heterogeneous methods for clustering can help us better understand the evolution of each of the types apart from theevolution of the network as a whole. In fact, the problem of clustering and evolution diagnosis are closely related because of the ability of the clustering process to summarize the network and provide insights into the changes in the objects over time. We present such a tightly integrated method for clustering and evolution diagnosis of heterogeneous bibliographic information networks. We present an algorithm, ENetClus, which performs such an agglomerative evolutionary clustering which is able to show variations in the clusters over time with a temporal smoothness approach. Previous work on clustering networks is either based on homogeneous graphs with evolution, or it does not account for evolution in the process of clustering heterogeneous networks. This paper provides the first framework for evolutionsensitive clustering and diagnosis of heterogeneous information networks. The ENetClus algorithm generates consistent typedclusterings across time, which can be used for further evolution diagnosis and insights. The framework of the algorithm is specifically designed in order to facilitate insights about the evolution process. We use this technique in order to provide novel insights about bibliographic information networks.
AB - In this paper, we study the problem of evolutionary clustering of multi-typed objects in a heterogeneous bibliographic network. The traditional methods of homogeneous clustering methods do not result in a good typed-clustering. The design of heterogeneous methods for clustering can help us better understand the evolution of each of the types apart from theevolution of the network as a whole. In fact, the problem of clustering and evolution diagnosis are closely related because of the ability of the clustering process to summarize the network and provide insights into the changes in the objects over time. We present such a tightly integrated method for clustering and evolution diagnosis of heterogeneous bibliographic information networks. We present an algorithm, ENetClus, which performs such an agglomerative evolutionary clustering which is able to show variations in the clusters over time with a temporal smoothness approach. Previous work on clustering networks is either based on homogeneous graphs with evolution, or it does not account for evolution in the process of clustering heterogeneous networks. This paper provides the first framework for evolutionsensitive clustering and diagnosis of heterogeneous information networks. The ENetClus algorithm generates consistent typedclusterings across time, which can be used for further evolution diagnosis and insights. The framework of the algorithm is specifically designed in order to facilitate insights about the evolution process. We use this technique in order to provide novel insights about bibliographic information networks.
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U2 - 10.1109/ASONAM.2011.12
DO - 10.1109/ASONAM.2011.12
M3 - Conference contribution
AN - SCOPUS:80052731815
SN - 9780769543758
T3 - Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
SP - 63
EP - 70
BT - Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
T2 - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
Y2 - 25 July 2011 through 27 July 2011
ER -