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Title: Disrupting Diffusion: Critical Nodes in Network
We formulate and study the problem of identifying nodes whose absence can maximally disrupt network-diffusion under the independent cascade model. We refer to such nodes as critical nodes. We present the notion of impact and characterize critical nodes based on this notion. Informally, impact of a set of nodes quantifies the necessity of the nodes in the diffusion process. We prove that the impact is monotonic. Interestingly, unlike similar formulation of critical edges in the context of Linear Threshold diffusion model, impact is neither submodular nor supermodular. Furthermore, we prove that the problem of finding a set of nodes which maximizes impact is NP-Hard. Hence, we develop heuristics that rely on submodular approximations of the impact function. We empirically evaluate our heuristics by comparing the level of disruption achieved by identifying and removing critical nodes as opposed to that achieved by removing the most influential nodes.  more » « less
Award ID(s):
1934884
NSF-PAR ID:
10288394
Author(s) / Creator(s):
; ; ;
Editor(s):
He, Jing; Purohit, Hemant; Huang, Guangyan; Gao, Xiaoying; Deng, Ke
Date Published:
Journal Name:
IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI/IAT 2020,
Page Range / eLocation ID:
397-404
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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