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- W2742303955 abstract "We study the problem of inferring the graph structure of a network using knowledge of information cascades in the network. Unlike previous studies, which assume knowledge of the distributions of information diffusion across edges in the network, we only require that diffusion along different edges in the network be independent together with limited information on their distributions (e.g., just the means). We introduce the concept of a separating vertex set for a graph, which is a set of vertices in which for any two given distinct vertices of the graph, one can find a vertex whose distance to them are different. We show that a necessary condition for reconstructing a tree perfectly using distance information between pairs of vertices is given by the size of an observed separating vertex set. We then propose an algorithm to recover the tree structure using infection times, whose differences have means corresponding to the distance between two vertices. To improve the accuracy, we propose the concept of redundant vertices, which allows us to perform averaging to better estimate the distance between two vertices. Though the theory is developed mainly for trees, we demonstrate how the algorithm can be extended heuristically to general graphs. Simulation results suggest that our proposed algorithm performs better than some current state-of-the-art network reconstruction methods." @default.
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- W2742303955 date "2017-06-01" @default.
- W2742303955 modified "2023-09-26" @default.
- W2742303955 title "Inferring network topology from information cascades" @default.
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- W2742303955 doi "https://doi.org/10.1109/isit.2017.8006980" @default.
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