research-article

Dynamic community detection including node attributes

Authors:

Renny Márquez,

Richard WeberAuthors Info & Claims

Volume 223, Issue C

https://doi.org/10.1016/j.eswa.2023.119791

Published: 01 August 2023 Publication History

Abstract

Community detection is an important task in social network analysis. It is generally based on the links of a static network, where groups of connected nodes can be found. Real-world problems, however, are often characterized by behavior that changes over time. In such cases, we need dynamic community detection algorithms because they better capture the underlying dynamics. Furthermore, the inclusion of node attributes provides a more robust approach since dynamic attribute values could also indicate changes in the communities. We propose an algorithm for COmmunity DEtection in Dynamic Attributed NETworks (CoDeDANet), which allows us to find groups in dynamic attributed networks using both the link and node information. In the first phase, based on spectral clustering, the attributes’ importance is optimized in a setting that joins the nodes’ features with a topological structure. In a second phase, tensors are used to consider not only current but also past information. The algorithm was tested on four synthetic networks and two real-world social networks. The results show that CoDeDANet outperforms the other state-of-the-art community detection algorithms.

Highlights

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Community detection is an important task in Social Network Analysis.

•

Social networks are dynamic and their structure changes over time.

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Nodes’ attributes, as well as links, are important to identify such changes.

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Using nodes’ information enhances performance of dynamic community detection.

References

[1]

Abdrabbah S.B., Ayachi R., Amor N.B., Collaborative filtering based on dynamic community detection, Proceedings of the 2nd international conference on dynamic networks and knowledge discovery, Vol. 1229, CEUR-WS.org, Aachen, DEU, 2014, pp. 85–106. https://dl.acm.org/doi/10.5555/3053802.3053813.

[2]

Al-sharoa E., Al-khassaweneh M., Aviyente S., Tensor based temporal and multilayer community detection for studying brain dynamics during resting state fMRI, IEEE Transactions on Biomedical Engineering 66 (3) (2019) 695–709,.

[3]

Arbelaitz O., Gurrutxaga I., Muguerza J., Pérez J., Perona I., An extensive comparative study of cluster validity indices, Pattern Recognition 46 (1) (2013) 243–256,.

Digital Library

[4]

Atay Y., Koc I., Babaoglu I., Kodaz H., Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms, Applied Soft Computing 50 (2017) 194–211,.

Digital Library

[5]

Bahadori S., Zare H., Moradi P., PODCD: Probabilistic overlapping dynamic community detection, Expert Systems with Applications 174 (2021),.

Digital Library

[6]

Bello G.A., Harenberg S., Agrawal A., Samatova N.F., Community detection in dynamic attributed graphs, in: Li J., Li X., Wang S., Li J., Sheng Q.Z. (Eds.), Advanced data mining and applications. ADMA 2016. Lectures notes in computer science, Springer, Cham, 2016, pp. 329–344,.

Digital Library

[7]

Blondel V.D., Guillaume J.-L., Lambiotte R., Lefebvre E., Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment 2008 (10) (2008),.

[8]

Bothorel C., Cruz J.D., Magnani M., Micenkova B., Clustering attributed graphs: models, measures and methods, Networking Science 3 (3) (2015) 408–444,.

[9]

Cao J., Jin D., Yang L., Dang J., Incorporating network structure with node contents for community detection on large networks using deep learning, Neurocomputing 297 (2018) 71–81,.

[10]

Chakraborty T., Dalmia A., Mukherjee A., Ganguly N., Metrics for community analysis: A survey, ACM Computing Surveys 50 (4) (2017) 1–37,.

Digital Library

[11]

Cheng F., Cui T., Su Y., Niu Y., Zhang X., A local information based multi-objective evolutionary algorithm for community detection in complex networks, Applied Soft Computing 69 (2018) 357–367,.

[12]

Chunaev P., Community detection in node-attributed social networks: A survey, Computer Science Review 37 (2020),.

[13]

Combe D., Largeron C., Géry M., Egyed-Zsigmond E., I-louvain: An attributed graph clustering method, in: Fromont E., De Bie T., van Leeuwen M. (Eds.), Advances in intelligent data analysis XIV. IDA 2015. Lecture notes in computer science, Springer, Cham, 2015, pp. 181–192,.

[14]

Dang, T. A., & Viennet, E. (2012). Community detection based on structural and attribute similarities. In The sixth international conference on digital society (ICDS 2012), Vol. 659 (pp. 7–12).

[15]

Danon L., Diaz-Guilera A., Duch J., Arenas A., Comparing community structure identification, Journal of Statistical Mechanics: Theory and Experiment 2005 (09) (2005),.

[16]

Ferrara E., De Meo P., Catanese S., Fiumara G., Detecting criminal organizations in mobile phone networks, Expert Systems with Applications 41 (13) (2014) 5733–5750,.

[17]

Girvan M., Newman M.E.J., Community structure in social and biological networks, Proceedings of the National Academy of Sciences of the United States of America 99 (12) (2002) 7821–7826,.

[18]

Huang Z., Zhong X., Wang Q., Gong M., Ma X., Detecting community in attributed networks by dynamically exploring node attributes and topological structure, Knowledge-Based Systems 196 (2020),.

[19]

Khanam K.Z., Srivastava G., Mago V., The homophily principle in social network analysis: A survey, Multimedia Tools and Applications (2022) 1–44,.

Digital Library

[20]

Kolda T.G., Bader B.W., Tensor decompositions and applications, SIAM Review 51 (3) (2009) 455–500,.

Digital Library

[21]

Lalwani D., Somayajulu D.V., Krishna P.R., A community driven social recommendation system, 2015 IEEE international conference on big data (Big Data), IEEE, 2015, pp. 821–826,.

Digital Library

[22]

Largeron C., Mougel P.-N., Benyahia O., Zaïane O.R., DANCer: dynamic attributed networks with community structure generation, Knowledge and Information Systems 53 (1) (2017) 109–151,.

Digital Library

[23]

Li W., Zhou X., Yang C., Fan Y., Wang Z., Liu Y., Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery, Information Fusion (ISSN ) 79 (2022) 110–123,.

Digital Library

[24]

Li W., Zhu H., Li S., Wang H., Dai H., Wang C., Jin Q., Evolutionary community discovery in dynamic social networks via resistance distance, Expert Systems with Applications (ISSN ) 171 (2021),.

Digital Library

[25]

Lu H., Sang X., Zhao Q., Lu J., Community detection algorithm based on nonnegative matrix factorization and pairwise constraints, Physica A: Statistical Mechanics and its Applications 545 (2020),.

[26]

Ma L., Gong M., Liu J., Cai Q., Jiao L., Multi-level learning based memetic algorithm for community detection, Applied Soft Computing 19 (2014) 121–133,.

[27]

Ma H., Liu Z., Zhang X., Zhang L., Jiang H., Balancing topology structure and node attribute in evolutionary multi-objective community detection for attributed networks, Knowledge-Based Systems 227 (2021),.

Digital Library

[28]

Mahmoud H., Masulli F., Rovetta S., Russo G., Community detection in protein-protein interaction networks using spectral and graph approaches, in: Formenti E., Tagliaferri R., Wit E. (Eds.), Computational intelligence methods for bioinformatics and biostatistics. CIBB 2013. Lecture dois in computer science, Springer, Cham, 2014, pp. 62–75,.

[29]

Mankad S., Michailidis G., Structural and functional discovery in dynamic networks with non-negative matrix factorization, Physical Review E 88 (4) (2013),.

[30]

Nath K., Roy S., Nandi S., Inovin: A fuzzy-rough approach for detecting overlapping communities with intrinsic structures in evolving networks, Applied Soft Computing 89 (2020),.

Digital Library

[31]

Newman M.E.J., Modularity and community structure in networks, Proceedings of the National Academy of Sciences of the United States of America 103 (23) (2006) 8577–8582,.

[32]

Newman M.E.J., Networks: An introduction, Oxford University Press, New York, 2010,.

[33]

Ozer M., Kim N., Davulcu H., Community detection in political twitter networks using nonnegative matrix factorization methods, 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) (2016) 81–88,.

[34]

Palla G., Barabási A.-L., Vicsek T., Quantifying social group evolution, Nature 446 (7136) (2007) 664–667,.

[35]

Pfeiffer III, J. J., Moreno, S., La Fond, T., Neville, J., & Gallagher, B. (2014). Attributed graph models: Modeling network structure with correlated attributes. In Proceedings of the 23rd international conference on world wide web (pp. 831–842). https://doi.org/10.1145/2566486.2567993.

[36]

Pourabbasi E., Majidnezhad V., Taghavi Afshord S., Jafari Y., A new single-chromosome evolutionary algorithm for community detection in complex networks by combining content and structural information, Expert Systems with Applications 186 (2021),.

Digital Library

[37]

Ríos S.A., Videla-Cavieres I.F., Generating groups of products using graph mining techniques, Procedia Computer Science 35 (2014) 730–738,.

[38]

Said A., Abbasi R.A., Maqbool O., Daud A., Aljohani N.R., CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks, Applied Soft Computing 63 (2018) 59–70,.

Digital Library

[39]

Shang R., Zhao K., Zhang W., Feng J., Li Y., Jiao L., Evolutionary multiobjective overlapping community detection based on similarity matrix and node correction, Applied Soft Computing 127 (2022),.

Digital Library

[40]

Sheikholeslami F., Giannakis G.B., Identification of overlapping communities via constrained egonet tensor decomposition, IEEE Transactions on Signal Processing 66 (21) (2018) 5730–5745,.

Digital Library

[41]

Tang F., Wang C., Su J., Wang Y., Spectral clustering-based community detection using graph distance and node attributes, Computational Statistics 35 (1) (2020) 69–94,.

Digital Library

[42]

Taya F., de Souza J., Thakor N.V., Bezerianos A., Comparison method for community detection on brain networks from neuroimaging data, Applied Network Science 1 (1) (2016) 1–20,.

[43]

Von Luxburg U., A tutorial on spectral clustering, Statistics and Computing 17 (4) (2007) 395–416,.

Digital Library

[44]

Wang X., Cao X., Jin D., Cao Y., He D., The (un)supervised NMF methods for discovering overlapping communities as well as hubs and outliers in networks, Physica A: Statistical Mechanics and its Applications 446 (2016) 22–34,.

[45]

Wu H., Gao L., Dong J., Yang X., Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks, PLoS One 9 (3) (2014),.

[46]

Zhou Y., Cheng H., Yu J.X., Graph clustering based on structural/attribute similarities, Proceedings of the VLDB Endowment 2 (1) (2009) 718–729,.

Digital Library

Cited By

Wang JZhou LWang XWang LLi S(2024)Attribute-sensitive community search over attributed heterogeneous information networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121153235:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121153

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Dynamic community detection including node attributes

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 223, Issue C

Aug 2023

1341 pages

ISSN:0957-4174

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 August 2023

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Cited By

Wang JZhou LWang XWang LLi S(2024)Attribute-sensitive community search over attributed heterogeneous information networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121153235:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121153

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