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Critical Nodes Identification in Large Networks: The Inclined and Detached Models

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In social networks, the departure of some users can lead to the dropout of others from the community in cascade. Therefore, the engagement of critical users can significantly influence the stability of a network. In the literature, the anchored/collapsed k-core problem is proposed, which aims to enlarge/collapse the community by anchoring/deleting certain nodes. While, in real social networks, nodes are usually associated with different preferences, such as close or conflict interest. Intuitively, a community will be more stable if more nodes share close interest and fewer of them carry conflict interest. However, most existing researches simply treat all users equally, and the inclination property is neglected. To fill the gap, in this paper, we propose two novel problems, named inclined anchored k-core (IAK) problem and minimum detached k-core (MDK) problem, to better characterize the real scenarios. We prove that both problems are NP-hard. To facilitate the computation, novel search strategies are proposed. Comprehensive experiments are conducted on 9 networks to demonstrate the effectiveness and efficiency of the proposed techniques.

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The paper is a journal extension of our WISE 2021 conference paper [4]. This work was supported by NSFC 61802345, ZJNSF LQ20F020007, ZJNSF LY21F020012 and Y202045024.

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Correspondence to Chen Chen.

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This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2021

Guest Editors: Hua Wang, Wenjie Zhang, Lei Zou, and Zakaria Maamar

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Sun, R., Chen, C., Liu, X. et al. Critical Nodes Identification in Large Networks: The Inclined and Detached Models. World Wide Web 25, 1315–1341 (2022).

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