Steve T. K. Jan

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Ensembles of graphs arise in several natural applications. Many techniques exist to compute frequent, dense subgraphs in these ensembles. In contrast, in this paper, we propose to 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(More)
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(More)
Given a social network, how to find communities of nodes based on their diffusive characteristics? There exist two important types of nodes, for information propagation: nodes that are influential (" kernel nodes "), and nodes that serve as " bridges " to boost the diffusion (" media nodes "). How to find these nodes and uncover connections between them? In(More)
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