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Due to the widespread interest in networks as a representation to investigate the properties of complex systems, there has been a great deal of interest in generative models of graph structure that can capture the properties of networks observed in the real world. Recent models have focused primarily on accurate characterization of sparse networks with(More)
Due to the recent availability of large complex networks, considerable analysis has focused on understanding and characterizing the properties of these networks. Scalable genera-tive graph models focus on modeling distributions of graphs that match real world network properties and scale to large datasets. Much work has focused on modeling networks with a(More)
—Mining the underlying patterns in gigantic and complex data is of great importance to data analysts. In this paper, we propose a motion pattern approach to mine frequent behaviors in trajectory data. Motion patterns, defined by a set of highly similar flow vector groups in a spatial locality, have been shown to be very effective in extracting dominant(More)
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