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Ego-centered community detection in directed and weighted networks

Published: 31 July 2017 Publication History

Abstract

Community detection is one of the most studied topics in Social Network Analysis. Research in this realm has predominantly focus on finding out communities by considering the network as a whole. That is, all nodes are put in the same pool to define central metrics for finding out communities while ignoring the particularity of some nodes and their impact. Yet, if the position of some nodes matters when defining the metrics (i.e. node centric approach), the found communities may differ and can make more sens in real life situations. For instance, identifying the communities based on drug dealers and their interactions with others sounds better than finding communities while ignoring the individuals status. The purpose of this paper is to detect ego-centered community, which is defined as a community built from a particular node. Our solution is set to combine both link direction and weight, and therefore, differs from many existing solutions. Basically, we rely on a metric called a quality function that uses link properties to assess the cohesion of identified groups. Our method detect communities that reflect not only the structure but the reality regarding to the interaction nature in terms of intensity. We implement our solution and use "Les Miserables" dataset to demonstrate the effectiveness of our solution.

References

[1]
Bohlin, L., Edler, D., Lancichinetti, A., & Rosvall, M. (2014). Community Detection and Visualization of Networks with the Map Equation Framework. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring Scholarly Impact: Methods and Practice (pp. 3--34). Cham: Springer International Publishing.
[2]
Chen, J., Zaane, O., & Goebel, R. (2009). Local community identification in social networks. In International conference on advances in social network analysis and mining (p. 237--242).
[3]
Clauset, A. (2005). Finding local community structure in networks. Physical Review E, 72(2), 026132.
[4]
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695.
[5]
Danisch, M., Guillaume, J., & Grand, B. L. (2014). Multi-ego-centered communities in practice. Social Netw. Analys. Mining, 4(1), 180.
[6]
Danisch, M., Guillaume, J.-L., & Le Grand, B. (2013). Unfolding Ego-Centered Community Structures with "A Similarity Approach". In Complex Networks IV: Proceedings of the 4th Workshop on Complex Networks CompleNet 2013 (pp. 145--153).
[7]
De Meo, P., Ferrara, E., Fiumara, G., & Provetti, A. (2014). Mixing local and global information for community detection in large networks. J. Comput. Syst. Sci., 80, 72--87.
[8]
Epasto, A., Lattanzi, S., Mirrokni, V., Sebe, I. O., Taei, A., & Verma, S. (2015). Ego-net community mining applied to friend suggestion. Proc. VLDB Endow., 9(4), 324--335.
[9]
Flake, G. W., Lawrence, S., Giles, C. L., & Coetzee, F. M. (2002). Self-organization and identification of web communities. Computer, 35(3), 66--71.
[10]
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proc. of the National Academy of Sciences of the USA, 99(12), 7821--7826.
[11]
Knuth, D. E. (1993). The stanford graphbase: A platform for combinatorial computing. New York, NY, USA: ACM.
[12]
Li, J., Wang, X., & Cui, Y. (2014). Uncovering the overlapping community structure of complex networks by maximal cliques. Physica A: Statistical Mechanics and its Applications, 415, 398--406.
[13]
Lu, Z., Wen, Y., & Cao, G. (2013). Community detection in weighted networks: Algorithms and applications. In Ieee international conference on pervasive computing and communications (percom) (pp. 179--184).
[14]
Mahmoud, H., Masulli, F., Rovetta, S., & Russo, G. (2014). Community Detection in Protein-Protein Interaction Networks Using Spectral and Graph Approaches. In E. Formenti, R. Tagliaferri, & E. Wit (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics: 10th International Meeting, CIBB 2013, Nice, France, June 20--22, 2013, Revised Selected Papers (pp. 62--75). Cham: Springer International Publishing.
[15]
Nefedov, N. (2011). Multiple-membership communities detection and its applications for mobile networks. INTECH Open Access Publisher.
[16]
Rees, B. S., & Gallagher, K. B. (2013). Ego-Clustering: Overlapping Community Detection via Merged Friendship-Groups. In T. Özyer, J. Rokne, G. Wagner, & A. H. Reuser (Eds.), The Influence of Technology on Social Network Analysis and Mining (pp. 1--20). Springer Vienna.
[17]
Rong, X., Chen, Z., Mei, Q., & Adar, E. (2016). Egoset: Exploiting word ego-networks and user-generated ontology for multifaceted set expansion. In Proceedings of the 9th acm international conference on web search and data mining (pp. 645--654).
[18]
Weng Lilian, Menczer Filippo, & Ahn Yong-Yeol. (2013, aug). Virality Prediction and Community Structure in Social Networks., 3, 2522.

Cited By

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  • (2020)Exploiting homophily to characterize communities in online social networksConcurrency and Computation: Practice and Experience10.1002/cpe.592933:8Online publication date: 4-Oct-2020
  • (2018)Ego-Community Evolution Tracking in Instant Messaging NetworksInnovations and Interdisciplinary Solutions for Underserved Areas10.1007/978-3-319-98878-8_2(13-22)Online publication date: 30-Aug-2018
  • (2018)Survey on Social Ego-Community DetectionComplex Networks and Their Applications VII10.1007/978-3-030-05414-4_31(388-399)Online publication date: 5-Dec-2018

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cover image ACM Conferences
ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
July 2017
698 pages
ISBN:9781450349932
DOI:10.1145/3110025
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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Published: 31 July 2017

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Cited By

View all
  • (2020)Exploiting homophily to characterize communities in online social networksConcurrency and Computation: Practice and Experience10.1002/cpe.592933:8Online publication date: 4-Oct-2020
  • (2018)Ego-Community Evolution Tracking in Instant Messaging NetworksInnovations and Interdisciplinary Solutions for Underserved Areas10.1007/978-3-319-98878-8_2(13-22)Online publication date: 30-Aug-2018
  • (2018)Survey on Social Ego-Community DetectionComplex Networks and Their Applications VII10.1007/978-3-030-05414-4_31(388-399)Online publication date: 5-Dec-2018

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