skip to main content
research-article

Community Detection by Motif-Aware Label Propagation

Published: 09 February 2020 Publication History

Abstract

Community detection (or graph clustering) is crucial for unraveling the structural properties of complex networks. As an important technique in community detection, label propagation has shown the advantage of finding a good community structure with nearly linear time complexity. However, despite the progress that has been made, there are still several important issues that have not been properly addressed. First, the label propagation typically proceeds over the lower order structure of the network and only the direct one-hop connections between nodes are taken into consideration. Unfortunately, the higher order structure that may encode design principle of the network and be crucial for community detection is neglected under this regime. Second, the stability of the identified community structure may also be seriously affected by the inherent randomness in the label propagation process. To tackle the above issues, this article proposes a Motif-Aware Weighted Label Propagation method for community detection. We focus on triangles within the network, but our technique extends to other kinds of motifs as well. Specifically, the motif-based higher order structure mining is conducted to capture structural characteristics of the network. First, the motif of interest (locally meaningful pattern) is identified, and then, the motif-based hypergraph can be constructed to encode the higher order connections. To further utilize the structural information of the network, a re-weighted network is designed, which unifies both the higher order structure and the original lower order structure. Accordingly, a novel voting strategy termed NaS (considering both <underline>N</underline>umber <underline>a</underline>nd <underline>S</underline>trength of connections) is proposed to update node labels during the label propagation process. In this way, the random label selection can be effectively eliminated, yielding more stable community structures. Experimental results on multiple real-world datasets have shown the superiority of the proposed method.

References

[1]
Alex Arenas, Alberto Fernandez, Santo Fortunato, and Sergio Gomez. 2008. Motif-based communities in complex networks. Journal of Physics A: Mathematical and Theoretical 41, 22 (2008), 224001.
[2]
Punam Bedi and Chhavi Sharma. 2016. Community detection in social networks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6, 3 (2016), 115--135.
[3]
Austin R. Benson, David F. Gleich, and Jure Leskovec. 2016. Higher-order organization of complex networks. Science 353, 6295 (2016), 163--166.
[4]
Oualid Boutemine and Mohamed Bouguessa. 2017. Mining community structures in multidimensional networks. ACM Transactions on Knowledge Discovery from Data 11, 4 (2017), 51.
[5]
Tanmoy Chakraborty, Sriram Srinivasan, Niloy Ganguly, Animesh Mukherjee, and Sanjukta Bhowmick. 2016. Permanence and community structure in complex networks. ACM Transactions on Knowledge Discovery from Data 11, 2 (2016), 14.
[6]
Zheng Chen, Xinli Yu, Bo Song, Jianliang Gao, Xiaohua Hu, and Wei-Shih Yang. 2017. Community-based network alignment for large attributed network. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 587--596.
[7]
Sofie Demeyer, Tom Michoel, Jan Fostier, Pieter Audenaert, Mario Pickavet, and Piet Demeester. 2013. The index-based subgraph matching algorithm (ISMA): Fast subgraph enumeration in large networks using optimized search trees. PLOS One 8, 4 (2013), e61183.
[8]
Gary William Flake, Steve Lawrence, C. Lee Giles, and Frans M. Coetzee. 2002. Self-organization and identification of web communities. Computer 35, 3 (2002), 66--70.
[9]
Santo Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3--5 (2010), 75--174.
[10]
Michelle Girvan and Mark E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 12 (2002), 7821--7826.
[11]
Paul W. Holland and Samuel Leinhardt. 1977. A method for detecting structure in sociometric data. In Social Networks. Elsevier, 411--432.
[12]
Maarten Houbraken, Sofie Demeyer, Tom Michoel, Pieter Audenaert, Didier Colle, and Mario Pickavet. 2014. The index-based subgraph matching algorithm with general symmetries (ISMAGS): Exploiting symmetry for faster subgraph enumeration. PLOS One 9, 5 (2014), e97896.
[13]
Ling Huang, Hong-Yang Chao, and Guangqiang Xie. 2020. MuMod: A micro-unit connection approach for hybrid-order community detection. In Proceedings of the AAAI.
[14]
Ling Huang, Chang-Dong Wang, and Hong-Yang Chao. 2018. A harmonic motif modularity approach for multi-layer network community detection. In Proceedings of the IEEE International Conference on Data Mining (ICDM’18). 1043--1048.
[15]
Ling Huang, Chang-Dong Wang, and Hong-Yang Chao. 2019. Higher-order multi-layer community detection. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), the 31st Innovative Applications of Artificial Intelligence Conference (IAAI’19), the 9th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI’19). 9945--9946.
[16]
Ling Huang, Chang-Dong Wang, and Hong-Yang Chao. In Press 2019. oComm: Overlapping community detection in multi-view brain network. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[17]
Zahra Razaghi Moghadam Kashani, Hayedeh Ahrabian, Elahe Elahi, Abbas Nowzari-Dalini, Elnaz Saberi Ansari, Sahar Asadi, Shahin Mohammadi, Falk Schreiber, and Ali Masoudi-Nejad. 2009. Kavosh: A new algorithm for finding network motifs. BMC Bioinformatics 10, 1 (2009), 318.
[18]
Nadav Kashtan, Shalev Itzkovitz, Ron Milo, and Uri Alon. 2004. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20, 11 (2004), 1746--1758.
[19]
Matthieu Latapy. 2008. Main-memory triangle computations for very large (sparse (power-law)) graphs. Theoretical Computer Science 407, 1--3 (2008), 458--473.
[20]
Pei-Zhen Li, Yue-Xin Cai, Chang-Dong Wang, Mao-Jin Liang, and Yi-Qing Zheng. 2019. Higher-order brain network analysis for auditory disease. Neural Processing Letters 49, 3 (2019), 879--897.
[21]
Pei-Zhen Li, Ling Huang, Chang-Dong Wang, Dong Huang, and Jian-Huang Lai. 2018. Community detection using attribute homogenous motif. IEEE Access 6 (2018), 47707--47716.
[22]
Pei-Zhen Li, Ling Huang, Chang-Dong Wang, and Jian-Huang Lai. 2019. EdMot: An edge enhancement approach for motif-aware community detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’19). 479--487.
[23]
Wenqing Lin, Xiaokui Xiao, Xing Xie, and Xiaoli Li. 2017. Network motif discovery: A GPU approach. IEEE Transactions on Knowledge and Data Engineering 29, 3 (2017), 513--528.
[24]
Liyuan Liu, Linli Xu, Zhen Wangy, and Enhong Chen. 2015. Community detection based on structure and content: A content propagation perspective. In Proceedings of the 2015 IEEE International Conference on Data Mining. IEEE, 271--280.
[25]
Hao Lou, Shenghong Li, and Yuxin Zhao. 2013. Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Physica A: Statistical Mechanics and its Applications 392, 14 (2013), 3095--3105.
[26]
Miller McPherson, Lynn Smith-Lovin, and James M. Cook. 2001. Birds of a feather: Homophily in social networks. Annual Review of Sociology 27, 1 (2001), 415--444.
[27]
Ron Milo, Shai Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri Chklovskii, and Uri Alon. 2002. Network motifs: Simple building blocks of complex networks. Science 298, 5594 (2002), 824--827.
[28]
Mark E. J. Newman. 2001. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences 98, 2 (2001), 404--409.
[29]
Mark E. J. Newman. 2004. Fast algorithm for detecting community structure in networks. Physical Review E 69, 6 (2004), 066133.
[30]
Mark E. J. Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical Review E 69, 2 (2004), 026113.
[31]
Mark E. J. Newman and Juyong Park. 2003. Why social networks are different from other types of networks. Physical Review E 68, 3 (2003), 036122.
[32]
Arnau Prat-Pérez, David Dominguez-Sal, Josep-M Brunat, and Josep-Lluis Larriba-Pey. 2016. Put three and three together: Triangle-driven community detection. ACM Transactions on Knowledge Discovery from Data 10, 3 (2016), 22.
[33]
Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76, 3 (2007), 036106.
[34]
Maryam Ramezani, Ali Khodadadi, and Hamid R. Rabiee. 2018. Community detection using diffusion information. ACM Transactions on Knowledge Discovery 12, 2, Article 20 (Jan. 2018), 22 pages.
[35]
Nachiketa Sahoo, Jamie Callan, Ramayya Krishnan, George Duncan, and Rema Padman. 2006. Incremental hierarchical clustering of text documents. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management. ACM, 357--366.
[36]
Falk Schreiber and Henning Schwöbbermeyer. 2005. MAVisto: A tool for the exploration of network motifs. Bioinformatics 21, 17 (2005), 3572--3574.
[37]
Ronghua Shang, Huan Liu, Licheng Jiao, and Amir M. Ghalamzan Esfahani. 2017. Community mining using three closely joint techniques based on community mutual membership and refinement strategy. Applied Soft Computing 61 (2017), 1060--1073.
[38]
Ronghua Shang, Shuang Luo, Weitong Zhang, Rustam Stolkin, and Licheng Jiao. 2016. A multiobjective evolutionary algorithm to find community structures based on affinity propagation. Physica A: Statistical Mechanics and its Applications 453 (2016), 203--227.
[39]
Ronghua Shang, Weitong Zhang, Licheng Jiao, Rustam Stolkin, and Yu Xue. 2017. A community integration strategy based on an improved modularity density increment for large-scale networks. Physica A: Statistical Mechanics and Its Applications 469 (2017), 471--485.
[40]
Jianbo Shi and Jitendra Malik. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 8 (2000), 888--905.
[41]
Chang Su, Xiaotao Jia, Xianzhong Xie, and Yue Yu. 2015. A new random-walk based label propagation community detection algorithm. In Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol. 1. IEEE, 137--140.
[42]
Lovro Šubelj and Marko Bajec. 2011. Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction. Physical Review E 83, 3 (2011), 036103.
[43]
Bing-Jie Sun, Huawei Shen, Jinhua Gao, Wentao Ouyang, and Xueqi Cheng. 2017. A non-negative symmetric encoder-decoder approach for community detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 597--606.
[44]
Chao Tong, Jianwei Niu, Jinming Wen, Zhongyu Xie, and Fu Peng. 2015. Weighted label propagation algorithm for overlapping community detection. In Proceedings of the 2015 IEEE International Conference on Communications (ICC’15). IEEE, 1238--1243.
[45]
Charalampos E. Tsourakakis, Jakub Pachocki, and Michael Mitzenmacher. 2017. Scalable motif-aware graph clustering. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1451--1460.
[46]
Chang-Dong Wang, Jian-Huang Lai, and Philip S. Yu. 2013. Dynamic community detection in weighted graph streams. In Proceedings of the 13th SIAM International Conference on Data Mining. 151--161.
[47]
Chang-Dong Wang, Jian-Huang Lai, and Philip S. Yu. 2014. NEIWalk: Community discovery in dynamic content-based networks. IEEE Transactions on Knowledge and Data Engineering 26, 7 (2014), 1734--1748.
[48]
Fei Wang, Tao Li, Xin Wang, Shenghuo Zhu, and Chris Ding. 2011. Community discovery using nonnegative matrix factorization. Data Mining and Knowledge Discovery 22, 3 (2011), 493--521.
[49]
Yue Wang, Xun Jian, Zhenhua Yang, and Jia Li. 2017. Query optimal k-plex based community in graphs. Data Science and Engineering 2, 4 (2017), 257--273.
[50]
Sebastian Wernicke. 2005. A faster algorithm for detecting network motifs. In Proceedings of the International Workshop on Algorithms in Bioinformatics. Springer, 165--177.
[51]
Sebastian Wernicke. 2006. Efficient detection of network motifs. IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, 4 (2006), 347--359.
[52]
Sebastian Wernicke and Florian Rasche. 2006. FANMOD: A tool for fast network motif detection. Bioinformatics 22, 9 (2006), 1152--1153.
[53]
Elisabeth Wong, Brittany Baur, Saad Quader, and Chun-Hsi Huang. 2011. Biological network motif detection: Principles and practice. Briefings in Bioinformatics 13, 2 (2011), 202--215.
[54]
Jierui Xie, Mingming Chen, and Boleslaw K. Szymanski. 2013. LabelrankT: Incremental community detection in dynamic networks via label propagation. In Proceedings of the Workshop on Dynamic Networks Management and Mining. ACM, 25--32.
[55]
Jierui Xie and Boleslaw K. Szymanski. 2011. Community detection using a neighborhood strength driven label propagation algorithm. In Proceedings of the 2011 IEEE Network Science Workshop. IEEE, 188--195.
[56]
J. Xie and B. K. Szymanski. 2013. LabelRank: A stabilized label propagation algorithm for community detection in networks. In Proceedings of the 2013 IEEE 2nd Network Science Workshop (NSW’13). 138--143.
[57]
Jierui Xie, Boleslaw K. Szymanski, and Xiaoming Liu. 2011. Slpa: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE, 344--349.
[58]
Ömer Nebil Yaveroğlu, Noël Malod-Dognin, Darren Davis, Zoran Levnajic, Vuk Janjic, Rasa Karapandza, Aleksandar Stojmirovic, and Nataša Pržulj. 2014. Revealing the hidden language of complex networks. Scientific Reports 4 (2014), 4547.
[59]
Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich. 2017. Local higher-order graph clustering. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 555--564.
[60]
Han Zhang, Chang-Dong Wang, Jian-Huang Lai, and Philip S. Yu. 2019. Community detection using multilayer edge mixture model. Knowledge and Information Systems 60, 2 (2019), 757--779.

Cited By

View all
  • (2024)DHONE: Density-based higher-order network embeddingInternational Journal of Modern Physics C10.1142/S012918312450133X35:10Online publication date: 8-Apr-2024
  • (2024)A Privacy-Preserving Computation Framework for Multisource Label Propagation ServicesIEEE Transactions on Services Computing10.1109/TSC.2024.3486196(1-14)Online publication date: 2024
  • (2024)Motif-Based Contrastive Learning for Community DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.336787335:9(11706-11719)Online publication date: Sep-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data  Volume 14, Issue 2
April 2020
322 pages
ISSN:1556-4681
EISSN:1556-472X
DOI:10.1145/3382774
Issue’s Table of Contents
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 February 2020
Accepted: 01 November 2019
Revised: 01 October 2019
Received: 01 September 2018
Published in TKDD Volume 14, Issue 2

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Community detection
  2. higher order structure
  3. label propagation
  4. motifs

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

  • Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program
  • Fundamental Research Funds for the Central Universities
  • NSFC
  • Guangdong Natural Science Funds for Distinguished Young Scholar

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)67
  • Downloads (Last 6 weeks)7
Reflects downloads up to 27 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)DHONE: Density-based higher-order network embeddingInternational Journal of Modern Physics C10.1142/S012918312450133X35:10Online publication date: 8-Apr-2024
  • (2024)A Privacy-Preserving Computation Framework for Multisource Label Propagation ServicesIEEE Transactions on Services Computing10.1109/TSC.2024.3486196(1-14)Online publication date: 2024
  • (2024)Motif-Based Contrastive Learning for Community DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.336787335:9(11706-11719)Online publication date: Sep-2024
  • (2024)Community Detection via Autoencoder-Like Nonnegative Tensor DecompositionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320190635:3(4179-4191)Online publication date: Mar-2024
  • (2024)Higher Order Fuzzy Membership in Motif Modularity OptimizationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.348271732:12(7143-7156)Online publication date: 1-Dec-2024
  • (2024)XLoCoFC: A Fast Fuzzy Community Detection Approach Based on Expandable Local Communities Through Max-Membership Degree PropagationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339206911:5(6022-6037)Online publication date: Oct-2024
  • (2024)Community Detection Using An Improved Label Propagation Algorithm2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825947(7031-7040)Online publication date: 15-Dec-2024
  • (2024)Motif-based community detection in heterogeneous multilayer networksScientific Reports10.1038/s41598-024-59120-514:1Online publication date: 16-Apr-2024
  • (2024)Multi-order graph clustering with adaptive node-level weight learningPattern Recognition10.1016/j.patcog.2024.110843(110843)Online publication date: Aug-2024
  • (2024)A depth-first search approach to detect the community structure of weighted networks using the neighbourhood proximity measureInternational Journal of Data Science and Analytics10.1007/s41060-024-00631-9Online publication date: 27-Sep-2024
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media