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.
- 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.Google ScholarCross Ref
- Punam Bedi and Chhavi Sharma. 2016. Community detection in social networks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6, 3 (2016), 115--135.Google ScholarDigital Library
- Austin R. Benson, David F. Gleich, and Jure Leskovec. 2016. Higher-order organization of complex networks. Science 353, 6295 (2016), 163--166.Google Scholar
- Oualid Boutemine and Mohamed Bouguessa. 2017. Mining community structures in multidimensional networks. ACM Transactions on Knowledge Discovery from Data 11, 4 (2017), 51.Google Scholar
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- Santo Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3--5 (2010), 75--174.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Paul W. Holland and Samuel Leinhardt. 1977. A method for detecting structure in sociometric data. In Social Networks. Elsevier, 411--432.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- Matthieu Latapy. 2008. Main-memory triangle computations for very large (sparse (power-law)) graphs. Theoretical Computer Science 407, 1--3 (2008), 458--473.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- Mark E. J. Newman. 2001. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences 98, 2 (2001), 404--409.Google ScholarCross Ref
- Mark E. J. Newman. 2004. Fast algorithm for detecting community structure in networks. Physical Review E 69, 6 (2004), 066133.Google ScholarCross Ref
- Mark E. J. Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical Review E 69, 2 (2004), 026113.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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. DOI:https://doi.org/10.1145/3110215Google Scholar
- 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.Google ScholarDigital Library
- Falk Schreiber and Henning Schwöbbermeyer. 2005. MAVisto: A tool for the exploration of network motifs. Bioinformatics 21, 17 (2005), 3572--3574.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- Jianbo Shi and Jitendra Malik. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 8 (2000), 888--905.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Sebastian Wernicke. 2005. A faster algorithm for detecting network motifs. In Proceedings of the International Workshop on Algorithms in Bioinformatics. Springer, 165--177.Google ScholarDigital Library
- Sebastian Wernicke. 2006. Efficient detection of network motifs. IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, 4 (2006), 347--359..Google ScholarDigital Library
- Sebastian Wernicke and Florian Rasche. 2006. FANMOD: A tool for fast network motif detection. Bioinformatics 22, 9 (2006), 1152--1153.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
- 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. DOI:https://doi.org/10.1109/NSW.2013.6609210Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarDigital Library
Index Terms
- Community Detection by Motif-Aware Label Propagation
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