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CR-Graph: Community Reinforcement for Accurate Community Detection

Published: 19 October 2020 Publication History

Abstract

In this paper, we present CR-Graph (community reinforcement on graphs), a novel method that helps existing algorithms to perform more-accurate community detection (CD). Toward this end, CR-Graph strengthens the community structure of a given original graph by adding non-existent predicted intra-community edges and deleting existing predicted inter-community edges. To design CR-Graph, we propose the following two strategies: (1) predicting intra-community and inter-community edges (i.e., the type of edges) and (2) determining the amount of edges to be added/deleted. To show the effectiveness of CR-Graph, we conduct extensive experiments with various CD algorithms on 7 synthetic and 4 real-world graphs. The results demonstrate that CR-Graph improves the accuracy of all underlying CD algorithms universally and consistently.

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Cited By

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  • (2023)A Framework for Accurate Community Detection on Signed Networks Using Adversarial LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323110435:11(10937-10951)Online publication date: 1-Nov-2023
  • (2021)Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00137(1150-1155)Online publication date: Dec-2021

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  1. CR-Graph: Community Reinforcement for Accurate Community Detection

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      cover image ACM Conferences
      CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
      October 2020
      3619 pages
      ISBN:9781450368599
      DOI:10.1145/3340531
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 19 October 2020

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      Author Tags

      1. community detection
      2. community reinforcement
      3. inter-community edges
      4. intra-community edges
      5. preprocessing

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      Funding Sources

      • National Research Foundation of Korea grant funded by the Korea government
      • Next-Generation Information Computing Development Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT
      • National Research Foundation of Korea

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      • (2023)A Framework for Accurate Community Detection on Signed Networks Using Adversarial LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323110435:11(10937-10951)Online publication date: 1-Nov-2023
      • (2021)Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00137(1150-1155)Online publication date: Dec-2021

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