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2015 IEEE International Conference on Data Mining Workshop (ICDMW) (2015)
Atlantic City, NJ, USA
Nov. 14, 2015 to Nov. 17, 2015
ISSN: 2375-9259
ISBN: 978-1-4673-8492-6
pp: 508-515
Ensembles of graphs arise in several natural applications, such as mobility tracking, computational biology, socialnetworks, and epidemiology. A common problem addressed by many existing mining techniques is to identify subgraphs of interest in these ensembles. In contrast, in this paper, we propose to quickly discover maximally variable regions of the graphs, i.e., sets of nodes that induce very different subgraphs across the ensemble. We first develop two intuitive and novel definitions of such node sets, which we then show can be efficiently enumerated using a level-wise algorithm. Finally, using extensive experiments on multiple real datasets, we show how these sets capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.
Data mining, Social network services, Entropy, Heuristic algorithms, Conferences, Computational biology, TV,UC, Unstable Community, Graph Mining, Subgraph Divergence, Scaled Subgraph Divergence
Ahsanur Rahman, Steve Jan, Hyunju Kim, B. Aditya Prakash, T. M. Murali, "Mining Unstable Communities from Network Ensembles", 2015 IEEE International Conference on Data Mining Workshop (ICDMW), vol. 00, no. , pp. 508-515, 2015, doi:10.1109/ICDMW.2015.87
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