Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Comprehensive Review of Community Detection in Graphs (2309.11798v5)

Published 21 Sep 2023 in cs.SI and cs.LG

Abstract: The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a thorough exposition of various community detection methods from perspectives of modularity-based method, spectral clustering, probabilistic modelling, and deep learning. Along with the methods, a new community detection method designed by us is also presented. Additionally, the performance of these methods on the datasets with and without ground truth is compared. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (184)
  1. Political book dataset page. http://www.orgnet.com/divided.html. Last accessed 2023/04/10.
  2. Us airports dataset page. https://toreopsahl.com/datasets/#usairports. Last accessed 2023/04/09.
  3. E. Abbe. Community detection and stochastic block models: recent developments. The Journal of Machine Learning Research, 18(1):6446–6531, 2017.
  4. Exact recovery in the stochastic block model. IEEE Transactions on Information Theory, 62(1):471–487, 2015.
  5. Community detection model for dynamic networks based on hidden markov model and evolutionary algorithm. Artificial Intelligence Review, 56(9):9665–9697, 2023.
  6. S. Akbar and S. K. Saritha. Towards quantum computing based community detection. Computer Science Review, 38:100313, 2020.
  7. A multiagent reinforcement learning algorithm to solve the community detection problem. Signal and Data Processing, 19(1):87–100, 2022.
  8. Podcd: Probabilistic overlapping dynamic community detection. Expert Systems with Applications, 174:114650, 2021.
  9. Structure enhanced deep clustering network via a weighted neighbourhood auto-encoder. Neural Networks, 155:144–154, 2022.
  10. M. J. Barber. Modularity and community detection in bipartite networks. Physical Review E, 76(6):066102, 2007.
  11. Detecting network communities by propagating labels under constraints. Physical review E, 80(2):026129, 2009.
  12. Simplicial closure and higher-order link prediction. Proceedings of the National Academy of Sciences, 115(48):E11221–E11230, 2018.
  13. Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding. Computers in Biology and Medicine, 138:104933, 2021.
  14. A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix. Cluster Computing, pp.  1–20, 2022.
  15. The state of the art of deep learning models in medical science and their challenges. Multimedia Systems, 27(4):599–613, 2021.
  16. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, 2003.
  17. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008.
  18. J. Bruna and X. Li. Community detection with graph neural networks. stat, 1050:27, 2017.
  19. Greedy recursive spectral bisection for modularity-bound hierarchical divisive community detection. 2024.
  20. Learning community embedding with community detection and node embedding on graphs. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, pp.  377–386, New York, NY, USA, 2017. Association for Computing Machinery. ISBN 9781450349185. doi: 10.1145/3132847.3132925. URL https://doi.org/10.1145/3132847.3132925.
  21. A feasible community detection algorithm for multilayer networks. Symmetry, 12(2):223, 2020a.
  22. The diagnostic accuracy of the ascertain dementia 8 questionnaire for detecting cognitive impairment in primary care in the community, clinics and hospitals: a systematic review and meta-analysis. Family Practice, 35(3):239–246, 2018.
  23. Community detection via maximization of modularity and its variants. IEEE Transactions on Computational Social Systems, 1(1):46–65, 2014.
  24. Community detection based on deepwalk model in large-scale networks. Security and Communication Networks, 2020:1–13, 2020b.
  25. Supervised community detection with line graph neural networks. arXiv preprint arXiv:1705.08415, 2017.
  26. Comparing community detection algorithms in psychometric networks: A monte carlo simulation. Behavior Research Methods, pp.  1–21, 2023.
  27. Aurélio Ribeiro Costa. Towards modularity optimization using reinforcement learning to community detection in dynamic social networks. arXiv preprint arXiv:2111.15623, 2021.
  28. R. D’Amore. Expertise community detection. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp.  498–499, 2004.
  29. Information retrieval and extraction on covid-19 clinical articles using graph community detection and bio-bert embeddings. In ACL 2020 Workshop on Natural Language Processing for COVID-19 (NLP-COVID), 2020.
  30. Unveiling the hierarchical structure of music by multi-resolution community detection. Transactions of the International Society for Music Information Retrieval, 3(1):82–97, 2020.
  31. Generalized louvain method for community detection in large networks. In 2011 11th international conference on intelligent systems design and applications, pp.  88–93. IEEE, 2011.
  32. R. de Sapienza Luna. COMMUNITY DETECTION ON NODE-ATTRIBUTED NETWORKS USING SELF-LEARNING AND GRAPH NEURAL NETWORKS. PhD thesis, Universidade Federal do Rio de Janeiro, 2023.
  33. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1):1–22, 1977.
  34. P. A. Devijver. Baum’s forward-backward algorithm revisited. Pattern Recognition Letters, 3(6):369–373, 1985.
  35. A. Dridi. Information retrieval framework based on social document profile. In Joint Proceedings of the CAiSE 2014 Forum and CAiSE 2014 Doctoral Consortium: co-located with the 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014), volume 1164, pp.  1–8. CEUR, 2014.
  36. S. Dutt. New faster kernighan-lin-type graph-partitioning algorithms. In Proceedings of 1993 International Conference on Computer Aided Design (ICCAD), pp.  370–377. IEEE, 1993.
  37. Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982, 2020.
  38. Node importance based label propagation algorithm for overlapping community detection in networks. Expert Systems with Applications, 162:113020, 2020.
  39. A novel network core structure extraction algorithm utilized variational autoencoder for community detection. Expert Systems with Applications, 222:119775, 2023.
  40. S. Fortunato. Community detection in graphs. Physics Reports, 486(3-5):75–174, 2010.
  41. L. Freeman. The development of social network analysis, volume 1. A Study in the Sociology of Science, 2004.
  42. Park K Fung. Infoflow: A distributed algorithm to detect communities according to the map equation. Big Data and Cognitive Computing, 3(3):42, 2019.
  43. Improving the louvain algorithm for community detection with modularity maximization. In Artificial Evolution: 11th International Conference, Evolution Artificielle, EA 2013, Bordeaux, France, October 21-23, 2013. Revised Selected Papers 11, pp.  145–156. Springer, 2014.
  44. Markov chain monte carlo (mcmc) sampling methods to determine optimal models, model resolution and model choice for earth science problems. Marine and Petroleum Geology, 26(4):525–535, 2009.
  45. A. Gandomi and M. Haider. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2):137–144, 2015.
  46. Distributed louvain algorithm for graph community detection. In 2018 IEEE international parallel and distributed processing symposium (IPDPS), pp.  885–895. IEEE, 2018.
  47. M. Girvan and M. E. Newman. Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12):7821–7826, 2002a.
  48. M. Girvan and M. E. Newman. Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12):7821–7826, 2002b.
  49. J. F. Gleyze. Topological clustering for geographical networks. In Methods for Multilevel Analysis and Visualisation of Geographical Networks, pp.  33–53. Springer Netherlands, 2013.
  50. Deep learning. MIT press, 2016.
  51. Vast 2008 challenge: Introducing mini-challenges. In 2008 IEEE Symposium on Visual Analytics Science and Technology, pp.  195–196. IEEE, 2008.
  52. A. Grover and J. Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  855–864, 2016.
  53. Graphite: Iterative generative modeling of graphs. In International Conference on Machine Learning, pp.  2434–2444. PMLR, 2019.
  54. A spectral method for community detection in moderately sparse degree-corrected stochastic block models. Advances in Applied Probability, 49(3):686–721, 2017.
  55. An attentional-walk-based autoencoder for community detection. Applied Intelligence, 53(10):11505–11523, 2023.
  56. L. Hagen and A. B. Kahng. New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 11(9):1074–1085, 1992.
  57. Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder. Information Sciences, 608:1464–1479, 2022.
  58. A network-specific markov random field approach to community detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
  59. A network embedding-enhanced bayesian model for generalized community detection in complex networks. Information Sciences, 575:306–322, 2021.
  60. Community detection in the labelled stochastic block model. arXiv preprint arXiv:1209.2910, 2012.
  61. A label propagation-based method for community detection in directed signed social networks. Physica A: Statistical Mechanics and its Applications, 604:127875, 2022.
  62. Improving louvain algorithm for community detection. In 2016 International Conference on Artificial Intelligence and Engineering Applications, pp.  110–115. Atlantis Press, 2016.
  63. Improving louvain algorithm for community detection. In Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications, pp.  110–115. Atlantis Press, 2016/11. ISBN 978-94-6252-270-1. doi: 10.2991/aiea-16.2016.20. URL https://doi.org/10.2991/aiea-16.2016.20.
  64. Community detection in complex networks using node2vec with spectral clustering. Physica A: Statistical Mechanics and its Applications, 545:123633, 2020.
  65. A novel algorithm infomap-sa of detecting communities in complex networks. J. Commun., 10(7):503–511, 2015a.
  66. A novel algorithm infomap-sa of detecting communities in complex networks. J. Commun., 10(7):503–511, 2015b.
  67. An algorithm based on non-negative matrix factorization for detecting communities in networks. Mathematics, 12(4):619, 2024.
  68. Machine learning and deep learning. Electronic Markets, 31(3):685–695, 2021.
  69. Modeling with node degree preservation can accurately find communities. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 29, 2015.
  70. Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pp.  152–159, 2019.
  71. Modmrf: A modularity-based markov random field method for community detection. Neurocomputing, 405:218–228, 2020.
  72. B. W. Kernighan and S. Lin. An efficient heuristic procedure for partitioning graphs. The Bell System Technical Journal, 49(2):291–307, 1970.
  73. Unsupervised learning of joint embeddings for node representation and community detection. In Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II 21, pp.  19–35. Springer, 2021.
  74. Feature analysis using information retrieval, community detection and structural analysis methods in product line adoption. Journal of Systems and Software, 155:70–90, 2019.
  75. T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016a.
  76. T. N. Kipf and M. Welling. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016b.
  77. D. Knoke and S. Yang. Social network analysis. SAGE publications, 2019.
  78. Spectral redemption in clustering sparse networks. Proceedings of the National Academy of Sciences, 110(52):20935–20940, 2013.
  79. Symmetric nonnegative matrix factorization for graph clustering. In Proceedings of the 2012 SIAM international conference on data mining, pp.  106–117. SIAM, 2012.
  80. A. Lancichinetti and S. Fortunato. Community detection algorithms: A comparative analysis. Physical Review E, 80(5):056117, 2009.
  81. C. Lee and D. J. Wilkinson. A review of stochastic block models and extensions for graph clustering. Applied Network Science, 4(1):1–50, 2019.
  82. Algorithms for non-negative matrix factorization. Advances in neural information processing systems, 13, 2000.
  83. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international conference on World wide web, pp.  631–640, 2010.
  84. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014a.
  85. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014b.
  86. Towards real-time community detection in large networks. Physical Review E, 79(6):066107, 2009.
  87. Lpa-mni: an improved label propagation algorithm based on modularity and node importance for community detection. Entropy, 23(5):497, 2021.
  88. Community detection using revised medoid-shift based on knn. In International Conference on Intelligent Computing, pp.  345–353. Springer Nature Singapore, 2023a.
  89. Csat: Contrastive sampling-aggregating transformer for community detection in attribute-missing networks. IEEE Transactions on Computational Social Systems, 2023b.
  90. Edmot: An edge enhancement approach for motif-aware community detection. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp.  479–487, 2019.
  91. Local spectral clustering for overlapping community detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 12(2):1–27, 2018.
  92. Deep learning for community detection: progress, challenges and opportunities. arXiv preprint arXiv:2005.08225, 2020.
  93. Community detection based on community perspective and graph convolutional network. Expert Systems with Applications, 231:120748, 2023.
  94. Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Physica A: Statistical Mechanics and its Applications, 389(7):1493–1500, 2010.
  95. A regularized stochastic block model for the robust community detection in complex networks. Scientific reports, 9(1):13247, 2019.
  96. S. R. Mandala. Scalable and robust algorithms for clustering large scale networks. PhD thesis, PhD Thesis, 2013.
  97. Deep learning techniques: an overview. In Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, pp.  599–608, 2021.
  98. T. K. Moon. The expectation-maximization algorithm. IEEE Signal Processing Magazine, 13(6):47–60, 1996.
  99. Bayesian community detection. Neural computation, 24(9):2434–2456, 2012.
  100. List of deep learning models. In International conference on global research and education, pp.  202–214. Springer International Publishing, 2019.
  101. Stochastic block models and reconstruction. arXiv preprint arXiv:1202.1499, 2012.
  102. Social network community detection using agglomerative spectral clustering. Complexity, 2017, 2017.
  103. Community detection in social media: Performance and application considerations. Data Mining and Knowledge Discovery, 24:515–554, 2012a.
  104. Community detection in social media: Performance and application considerations. Data Mining and Knowledge Discovery, 24:515–554, 2012b.
  105. J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.
  106. A scalable community detection algorithm for large graphs using stochastic block models. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
  107. Partitioning sparse matrices with eigenvectors of graphs. SIAM Journal on Matrix Analysis and Applications, 11(3):430–452, 1990.
  108. Dynamic community detection based on evolutionary deepwalk. Applied Sciences, 12(22):11464, 2022.
  109. Scalable community detection with the louvain algorithm. In 2015 IEEE International Parallel and Distributed Processing Symposium, pp.  28–37. IEEE, 2015a.
  110. Scalable community detection with the louvain algorithm. In 2015 IEEE international parallel and distributed processing symposium, pp.  28–37. IEEE, 2015b.
  111. L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286, 1989.
  112. Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76(3):036106, 2007.
  113. R. Rossi and N. Ahmed. The network data repository with interactive graph analytics and visualization. In Proceedings of the AAAI conference on artificial intelligence, volume 29, 2015.
  114. Community detection algorithms in healthcare applications: A systematic review. IEEE Access, 2023.
  115. M. Rosvall and C. T. Bergstrom. An information-theoretic framework for resolving community structure in complex networks. Proceedings of the national academy of sciences, 104(18):7327–7331, 2007.
  116. M. Rosvall and C. T. Bergstrom. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4):1118–1123, 2008.
  117. Gemsec: Graph embedding with self clustering. In Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pp.  65–72, 2019.
  118. An api oriented open-source python framework for unsupervised learning on graphs. arXiv preprint arXiv:2003.04819, 10(3340531.3412757), 2020.
  119. Monitoring the effect of chemicals on biological communities. the biofilm as an interface. Analytical and Bioanalytical Chemistry, 387:1425–1434, 2007.
  120. S. Sarkar and A. Dong. Community detection in graphs using singular value decomposition. Physical Review E, 83(4):046114, 2011.
  121. Parallelizing louvain algorithm: Distributed memory challenges. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp.  695–701. IEEE, 2018.
  122. J. Scott. Social network analysis: developments, advances, and prospects. Social network analysis and mining, 1:21–26, 2011.
  123. C-blondel: an efficient louvain-based dynamic community detection algorithm. IEEE Transactions on Computational Social Systems, 7(2):308–318, 2020.
  124. Pmcdm: Privacy-preserving multiresolution community detection in multiplex networks. Knowledge-Based Systems, 244:108542, 2022.
  125. O. Shchur and S. Günnemann. Overlapping community detection with graph neural networks. arXiv preprint arXiv:1909.12201, 2019.
  126. Mode-seeking by medoidshifts. In 2007 IEEE 11th International Conference on Computer Vision, pp.  1–8, 2007. doi: 10.1109/ICCV.2007.4408978.
  127. Bayesian community detection for networks with covariates. arXiv preprint arXiv:2203.02090, 2022.
  128. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888–905, 2000.
  129. Konstantinos Sismanis. Overlapping community detection using graph attention networks. 2023.
  130. Embedding-based silhouette community detection. Machine Learning, 109(11):2161–2193, 2020.
  131. General optimization technique for high-quality community detection in complex networks. Physical Review E, 90(1):012811, 2014.
  132. Spectral partitioning works: Planar graphs and finite element meshes. Linear Algebra and its Applications, 421(2-3):284–305, 2007.
  133. Nonnegative matrix factorization based on node centrality for community detection. ACM Transactions on Knowledge Discovery from Data, 17(6):1–21, 2023.
  134. vgraph: A generative model for joint community detection and node representation learning. Advances in Neural Information Processing Systems, 32, 2019.
  135. Improved community structure detection using a modified fine-tuning strategy. Europhysics Letters, 86(2):28004, 2009.
  136. From louvain to leiden: guaranteeing well-connected communities. Scientific reports, 9(1):5233, 2019a.
  137. Vincent A Traag. Faster unfolding of communities: Speeding up the louvain algorithm. Physical Review E, 92(3):032801, 2015.
  138. Large network community detection by fast label propagation. Scientific Reports, 13(1):2701, 2023.
  139. From louvain to leiden: guaranteeing well-connected communities. Scientific reports, 9(1):5233, 2019b.
  140. A unified framework for community detection and network representation learning. IEEE Transactions on Knowledge and Data Engineering, 31(6):1051–1065, 2018.
  141. Community detection using spectral clustering on sparse geosocial data. SIAM Journal on Applied Mathematics, 73(1):67–83, 2013.
  142. Scalable spectral clustering for overlapping community detection in large-scale networks. IEEE Transactions on Knowledge and Data Engineering, 32(4):754–767, 2019.
  143. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
  144. Efficient community detection in large scale networks. In 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, pp.  669–674. IEEE, 2013.
  145. Blind community detection from low-rank excitations of a graph filter. IEEE Transactions on signal processing, 68:436–451, 2019.
  146. L. Waltman and N. J. Van Eck. A smart local moving algorithm for large-scale modularity-based community detection. The European Physical Journal B, 86:1–14, 2013.
  147. A graph-enhanced attention model for community detection in multiplex networks. Expert Systems with Applications, 230:120552, 2023.
  148. Lda-lpa: A community detection method based on topic model. In 2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT), pp.  726–731, 2021a. doi: 10.1109/CECIT53797.2021.00132.
  149. H. Wang and B. Raj. On the origin of deep learning. arXiv preprint arXiv:1702.07800, 2017.
  150. Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures. Neurocomputing, 72(1-3):134–141, 2008.
  151. Unsupervised learning for community detection in attributed networks based on graph convolutional network. Neurocomputing, 456:147–155, 2021b.
  152. Towards efficient hierarchical designs by ratio cut partitioning. In 1989 IEEE International Conference on Computer-Aided Design. Digest of Technical Papers, pp.  298–301. IEEE, 1989.
  153. Diagnostic accuracy of handheld electrocardiogram devices in detecting atrial fibrillation in adults in community versus hospital settings: a systematic review and meta-analysis. Heart, 106(16):1211–1217, 2020.
  154. A label propagation algorithm for community detection on high-mixed networks. Concurrency and Computation: Practice and Experience, 33(9):e6141, 2021.
  155. A distributed community detection algorithm for large scale networks under stochastic block models. Computational Statistics & Data Analysis, 187:107794, 2023.
  156. Clare: A semi-supervised community detection algorithm. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp.  2059–2069, 2022.
  157. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1):4–24, 2020.
  158. Community detection in multi-relational social networks. In Web Information Systems Engineering–WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part II 14, pp.  43–56. Springer, 2013.
  159. Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys (CSUR), 45(4):1–35, 2013.
  160. Community detection using a neighborhood strength driven label propagation algorithm. In 2011 IEEE Network Science Workshop, pp.  188–195. IEEE, 2011.
  161. Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962, 2020a.
  162. A community detection method based on local optimization in social networks. Ieee Network, 34(4):42–48, 2020b.
  163. Tns-lpa: An improved label propagation algorithm for community detection based on two-level neighbourhood similarity. IEEE Access, PP:1–1, 12 2020c. doi: 10.1109/ACCESS.2020.3045085.
  164. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018.
  165. Fast normalized cut with linear constraints. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.  2866–2873. IEEE, 2009.
  166. A model-based approach to attributed graph clustering. In Proceedings of the 2012 ACM SIGMOD international conference on management of data, pp.  505–516, 2012.
  167. Topics in dynamic research communities: An exploratory study for the field of information retrieval. Journal of Informetrics, 6(1):140–153, 2012.
  168. Community detection in networks with node attributes. In 2013 IEEE 13th International Conference on Data Mining, pp.  1151–1156. IEEE, 2013.
  169. Combining link and content for community detection: a discriminative approach. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  927–936, 2009.
  170. A comparative analysis of community detection algorithms on artificial networks. Scientific reports, 6(1):30750, 2016.
  171. Accurate community detection in the stochastic block model via spectral algorithms. arXiv preprint arXiv:1412.7335, 2014.
  172. J. Zeng and H. Yu. A distributed infomap algorithm for scalable and high-quality community detection. In Proceedings of the 47th International Conference on Parallel Processing, pp.  1–11, 2018a.
  173. A distributed infomap algorithm for scalable and high-quality community detection. In Proceedings of the 47th International Conference on Parallel Processing, pp.  1–11, 2018b.
  174. An lda-based community structure discovery approach for large-scale social networks. In 2007 IEEE Intelligence and Security Informatics, pp.  200–207. IEEE, 2007.
  175. P. Zhang and C. Moore. Scalable detection of statistically significant communities and hierarchies, using message passing for modularity. Proceedings of the National Academy of Sciences, 111(51):18144–18149, 2014.
  176. Modularity optimization in community detection of complex networks. Europhysics Letters, 87(3):38002, 2009.
  177. Attributed graph clustering via adaptive graph convolution. arXiv preprint arXiv:1906.01210, 2019.
  178. Seal: Learning heuristics for community detection with generative adversarial networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.  1103–1113, 2020.
  179. Shne: Semantics and homophily preserving network embedding. IEEE Transactions on Neural Networks and Learning Systems, 2021.
  180. Self-adaptive louvain algorithm: Fast and stable community detection algorithm based on the principle of small probability event. Physica A: Statistical Mechanics and Its Applications, 506:975–986, 2018.
  181. Hierarchical attention network for attributed community detection of joint representation. Neural Computing and Applications, 34(7):5587–5601, 2022.
  182. Topic oriented community detection through social objects and link analysis in social networks. Knowledge-Based Systems, 26:164–173, 2012.
  183. Graph neural networks: A review of methods and applications. AI Open, 1:57–81, 2020.
  184. Learning from labeled and unlabeled data with label propagation. ProQuest number: information to all users, 2002.
Citations (8)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets