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Open AccessArticle

Tree Sampling for Detection of Information Source in Densely Connected Networks

Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 587;
Received: 23 April 2019 / Revised: 20 May 2019 / Accepted: 22 May 2019 / Published: 27 May 2019
(This article belongs to the Special Issue Data-Driven Network Security and Privacy)
We investigate the problem of source detection in information spreading throughout a densely-connected network. Previous works have been developed mostly for tree networks or applied the tree-network results to non-tree networks assuming that the infection occurs in the breadth first manner. However, these approaches result in low detection performance in densely-connected networks, since there is a substantial number of nodes that are infected through the non-shortest path. In this work, we take a two-step approach to the source detection problem in densely-connected networks. By introducing the concept of detour nodes, we first sample trees that the infection process likely follows and effectively compare the probability of the sampled trees. Our solution has low complexity of O ( n 2 log n ) , where n denotes the number of infected nodes, and thus can be applied to large-scale networks. Through extensive simulations including practical networks of the Internet autonomous system and power grid, we evaluate our solution in comparison with two well-known previous schemes and show that it achieves the best performance in densely-connected networks. View Full-Text
Keywords: source detection; estimation; approximation source detection; estimation; approximation
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Min, T.; Joo, C. Tree Sampling for Detection of Information Source in Densely Connected Networks. Electronics 2019, 8, 587.

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