Jithin K. Sreedharan

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Many networks in the real world are dynamic in nature: nodes enter, exit, and make and break connections with one another as time passes. Several random graph models of these networks are such that nodes have well-defined arrival times. It is natural to ask if, for a given random graph model, we can recover the arrival order of nodes, given information(More)
Inferring the node arrival sequence from a snapshot of a dynamic network is an important problem, with applications ranging from identifying sources of contagion to flow of capital in financial transaction networks. Variants of this problem have received significant recent research attention, including results on infeasibility of solution for prior(More)
In this paper we address the problem of finding top k eigenvalues and corresponding eigenvectors of symmetric graph matrices in networks in a distributed way. We propose a novel idea called complex power iterations in order to decompose the eigenvalues and eigenvectors at node level, analogous to time-frequency analysis in signal processing. At each node,(More)
Because of the significant increase in size and complexity of the networks, the distributed computation of eigenvalues and eigenvectors of graph matrices has become very challenging and yet it remains as important as before. In this paper we develop efficient distributed algorithms to detect, with higher resolution, closely situated eigenvalues and(More)
Social networks contain clusters of nodes centered at high-degree nodes and surrounded by low-degree nodes. Such a cluster structure of the networks is caused by the dependence (social relationships and interests) between nodes and possibly by heavytailed distributions of the node degrees. We consider the degree sequences generated by PageRank type sampling(More)
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