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NI-MLA: Node Importance based Multi-level Label Assignment strategy for community detection in sparse social graphs

Published: 15 March 2024 Publication History

Abstract

This research paper addresses the challenge of detecting communities in sparse social graphs and presents a novel approach that leverages node importance and label propagation. The proposed method consists of three phases: initialization, label assignment, and filtering. In the initialization phase, we carefully identify and designate key nodes using their local information and associate them with different labels. Subsequently, in the label assignment phase, the assigned labels are propagated to neighboring nodes, which are organized in a multilevel manner, taking into account their relevance and significance. Through the filtering phase, we effectively eliminate irrelevant labels, enhancing the accuracy of community assignments and resulting in an optimized community structure. To assess the effectiveness of our approach, we conducted experiments on both real-world networks and synthetic networks. A comparative analysis was performed against several established community detection techniques from existing literature. The results clearly demonstrate that our proposed algorithm surpasses existing methods in terms of accuracy and efficiency.

References

[1]
M. Azaouzi, D. Rhouma, and L. Ben Romdhane, "Community detection in large-scale social networks: state-of-the-art and future directions," Social Network Analysis and Mining, vol. 9, no. 1, Dec. 2019, publisher Copyright: © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.
[2]
S. Fortunato, "Community detection in graphs," Physics Reports, vol. 486, no. 3, pp. 75 -- 174, 2010.
[3]
T. Chakraborty, A. Dalmia, A. Mukherjee, and N. Ganguly, "Metrics for community analysis: A survey," ACM Comput. Surv., vol. 50, no. 4, aug 2017.
[4]
V. Tunali, "Large-scale network community detection using similarity-guided merge and refinement," IEEE Access, vol. 9, pp. 78 538--78 552, 2021.
[5]
S. Ahajjam, M. El Haddad, and H. Badir, "A new scalable leader-community detection approach for community detection in social networks," Social Networks, vol. 54, pp. 41 -- 49, 2018.
[6]
A. Bouyer and H. Roghani, "Lsmd: A fast and robust local community detection starting from low degree nodes in social networks," Future Generation Computer Systems, vol. 113, pp. 41--57, 2020.
[7]
W. W. Zachary, "An information flow model for conflict and fission in small groups," Journal of Anthropological Research, vol. 33, no. 4, pp. 452--473, 1977.
[8]
D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, "The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations," Behavioral Ecology and Sociobiology, vol. 54, no. 4, pp. 396--405, Sep 2003.
[9]
L. A. Adamic and N. Glance, "The political blogosphere and the 2004 u.s. election: Divided they blog," in Proceedings of the 3rd International Workshop on Link Discovery, ser. LinkKDD '05. New York, NY, USA: Association for Computing Machinery, 2005, p. 36--43.
[10]
M. E. J. Newman and M. Girvan, "Finding and evaluating community structure in networks," Phys. Rev. E, vol. 69, p. 026113, Feb 2004.
[11]
J. Stehlé, N. Voirin, A. Barrat, C. Cattuto, L. Isella, J.-F. Pinton, M. Quaggiotto, W. Van den Broeck, C. Régis, B. Lina, and P. Vanhems, "High-resolution measurements of face-to-face contact patterns in a primary school," PLOS ONE, vol. 6, no. 8, pp. 1--13, 08 2011.
[12]
R. Mastrandrea, J. Fournet, and A. Barrat, "Contact patterns in a high school: A comparison between data collected using wearable sensors, contact diaries and friendship surveys," PLOS ONE, vol. 10, pp. 1--26, 09 2015.
[13]
A. Lancichinetti, S. Fortunato, and F. Radicchi, "Benchmark graphs for testing community detection algorithms," Phys. Rev. E, vol. 78, p. 046110, Oct 2008.

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      cover image ACM Conferences
      ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
      November 2023
      835 pages
      ISBN:9798400704093
      DOI:10.1145/3625007
      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 the author(s) 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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      Published: 15 March 2024

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      Author Tags

      1. community detection
      2. node importance
      3. label propagation
      4. social networks

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