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Computer Science > Social and Information Networks
Title: An adaptation of InfoMap to absorbing random walks using absorption-scaled graphs
(Submitted on 21 Dec 2021 (v1), last revised 23 Apr 2024 (this version, v4))
Abstract: InfoMap is a popular approach to detect densely connected "communities" of nodes in networks. To detect such communities, InfoMap uses random walks and ideas from information theory. Motivated by the dynamics of disease spread on networks, whose nodes can have heterogeneous disease-removal rates, we adapt InfoMap to absorbing random walks. To do this, we use absorption-scaled graphs (in which edge weights are scaled according to absorption rates) and Markov time sweeping. One of our adaptations of InfoMap converges to the standard version of InfoMap in the limit in which the node-absorption rates approach $0$. We demonstrate that the community structure that one obtains using our adaptations of InfoMap can differ markedly from the community structure that one detects using methods that do not account for node-absorption rates. We also illustrate that the community structure that is induced by heterogeneous absorption rates can have important implications for susceptible-infected-recovered (SIR) dynamics on ring-lattice networks. For example, in some situations, the outbreak duration is maximized when a moderate number of nodes have large node-absorption rates.
Submission history
From: Esteban Vargas Bernal [view email][v1] Tue, 21 Dec 2021 03:03:03 GMT (1428kb,D)
[v2] Thu, 16 Mar 2023 03:13:43 GMT (1012kb)
[v3] Tue, 30 Jan 2024 21:36:42 GMT (823kb,D)
[v4] Tue, 23 Apr 2024 21:04:18 GMT (1509kb,D)
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