Evolutionary Clustering and Analysis of Bibliographic Networks

—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 the evolution 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 ...

• Type: Journal
• Year: 2011
• Where: ASUNAM
• Authors: Manish Gupta, Charu C. Aggarwal, Jiawei Han, Yizhou Sun
• Keywords: ASUNAM 2011, Bibliographic Information, Bibliographic Network, Heterogeneous Networks, Social Networks