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Community search has been extensively studied in large networks, such as Protein-Protein Interaction (PPI) networks, citation graphs, and collaboration networks. However, in terms of widely existing multi-valued networks, where each node has d ( d1) numerical attributes, almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute. To solve this problem, the concept of skyline community was presented, based on the concepts of k-core and skyline recently. The skyline community is defined as a maximal k-core that satisfies some influence constraints, which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks. However, the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute, and the computation complexity degrades exponentially as the number of attributes increases. In this work, we turn our attention to the general scenario where multiple nodes may have the same attribute value. Specifically, we first present an algorithm, called MICS, which can find all skyline communities in a multi-valued network. To improve computation efficiency, we then propose a dimension reduction based algorithm, called P-MICS, using the maximum entropy method. Our algorithm can significantly reduce the skyline community searching time, while is still able to find almost all cohesive skyline communities. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.


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Fast Skyline Community Search in Multi-Valued Networks

Show Author's information Dongxiao YuLifang Zhang( )Qi Luo( )Xiuzhen ChengJiguo YuZhipeng Cai
School of Computer Science and Technology, Shandong University, Qingdao 266237, China.
School of Computer Science and Technology, Qilu University of Technology, Jinan 250353, China.
Department of Computing Science, Georgia State University, Atlanta, GA 30303, USA.

Abstract

Community search has been extensively studied in large networks, such as Protein-Protein Interaction (PPI) networks, citation graphs, and collaboration networks. However, in terms of widely existing multi-valued networks, where each node has d ( d1) numerical attributes, almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute. To solve this problem, the concept of skyline community was presented, based on the concepts of k-core and skyline recently. The skyline community is defined as a maximal k-core that satisfies some influence constraints, which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks. However, the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute, and the computation complexity degrades exponentially as the number of attributes increases. In this work, we turn our attention to the general scenario where multiple nodes may have the same attribute value. Specifically, we first present an algorithm, called MICS, which can find all skyline communities in a multi-valued network. To improve computation efficiency, we then propose a dimension reduction based algorithm, called P-MICS, using the maximum entropy method. Our algorithm can significantly reduce the skyline community searching time, while is still able to find almost all cohesive skyline communities. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.

Keywords:

multi-valued graph, community search, skyline community
Received: 29 February 2020 Accepted: 07 March 2020 Published: 16 July 2020 Issue date: September 2020
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Publication history

Received: 29 February 2020
Accepted: 07 March 2020
Published: 16 July 2020
Issue date: September 2020

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© The author(s) 2020

Acknowledgements

This work was partially supported by the National Key R&D Program of China (No. 2019YFB2102600) and the National Natural Science Foundation of China (Nos. 61971269, 61832012, 616727321, and 61771289).

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