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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 2/2022

25.01.2022 | Regular Paper

Community detection combining topology and attribute information

verfasst von: Dan-Dan Lu, Ji Qi, Jie Yan, Zhong-Yuan Zhang

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2022

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Abstract

Community structures detection is critical in the analysis of features and functions of complex networks. Traditional methods are mostly concerned with the topology information of networks when conducting community detection, and can only describe the community structures from one aspect. For a more comprehensive analysis of the network, there is often attribute information available and it is a good complement to topology information. In this paper, we propose two parameter-free models based on nonnegative matrix factorization (NMF for short), Topology and Attribute NMF (TANMF for short) and Topology and Attribute Symmetrical NMF (TASNMF for short), combining topology information and attribute information for community structures detection. In addition, the multiplicative update rules are designed and the convergence is proved. Systematic experiments on both the synthetic and the real networks demonstrate the effectiveness and efficiency of our methods.
Vorheriger Artikel Computing top-k temporal closeness in temporal networks
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Fußnoten
Literatur
1.
2.
Bedi P, Sharma C (2016) Community detection in social networks, wiley interdisciplinary reviews. Data Min Knowl Discov 6(3):115–135CrossRef
3.
Tiziano S, Andrea G, Diego G, Tommaso G, Angelo B, Fabio C (2018) Complexity in neural and financial systems: from time-series to networks. Complexity 2018:1–2
4.
Cheng HM, Ning YZ, Yin Z, Yan C, Liu X, Zhang ZY (2018) Community detection in complex networks using link prediction. Mod Phys Lett B 32(3):1850004MathSciNetCrossRef
5.
Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRef
6.
Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis, physical review E statistical nonlinear and soft matter. Physics 80(5):056117
7.
Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113CrossRef
8.
Yang J, McAuley J, Leskovec J (2013) Community detection in networks with node attributes, In: 2013 IEEE 13th International conference on data mining, IEEE, pp 1151–1156
9.
Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791CrossRef
10.
Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization, In: Advances in neural information processing systems, pp 556–562
11.
Gu J, Hu H, Li H (2018) Local robust sparse representation for face recognition with single sample per person. IEEE/CAA J Autom Sin 5(2):547–554CrossRef
12.
Liu H, Wu Z, Li X, Cai D, Huang TS (2012) Constrained nonnegative matrix factorization for image representation. IEEE Trans Pattern Anal Mach Intel 34(7):1299–1311CrossRef
13.
Bai L, Guo J, Lan Y, Cheng X (2014) Local linear matrix factorization for document modeling, In: European conference on information retrieval, Springer, pp 398–411
14.
Tian Y, Li X, Wang K, Wang F-Y (2018) Training and testing object detectors with virtual images. IEEE/CAA J Autom Sin 5(2):539–546CrossRef
15.
Wang Y, Zhang Y (2013) Nonnegative matrix factorization: a comprehensive review. IEEE Trans Knowl Data Eng 25(6):1336–1353MathSciNetCrossRef
16.
Asim Y, Majeed A, Ghazal R, Raza B, Naeem W, Malik AK (2017) Community detection in networks using node attributes and modularity. Int J Adv Comput Sci Appl 8(1):382–388
17.
Farzi S, Kianian S (2018) A novel clustering algorithm for attributed graphs based on k-medoid algorithm. J Exp Theor Artif Intel 30(6):795–809CrossRef
18.
Li Y, Jia C, Kong X, Yang L, Yu J (2017) Locally weighted fusion of structural and attribute information in graph clustering. IEEE Trans Cybern 49(1):247–260CrossRef
19.
Yang T, Jin R, Chi Y, Zhu S (2009) 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, ACM, pp 927–936
20.
Xu, Z, Ke Y, Wang Y, Cheng H, Cheng J (2012) A model-based approach to attributed graph clustering, In: Proceedings of the 2012 ACM SIGMOD international conference on management of data, pp 505–516
21.
Ruan Y, Fuhry D, Parthasarathy S (2013) Efficient community detection in large networks using content and links, In: Proceedings of the 22nd international conference on World Wide Web, pp 1089–1098
22.
He D, Feng Z, Jin D, Wang X, Zhang W (2017) Joint identification of network communities and semantics via integrative modeling of network topologies and node contents, In: Thirty-First AAAI Conference on Artificial Intelligence
23.
Liu L, Xu L, Wangy Z, Chen E (2015) Community detection based on structure and content: a content propagation perspective, In: (2015) IEEE international conference on data mining. IEEE pp 271–280
24.
Bu Z, Gao G, Li H-J, Cao J (2017) Camas: a cluster-aware multiagent system for attributed graph clustering. Inf Fusion 37:10–21CrossRef
25.
Bu Z, Li H-J, Cao J, Wang Z, Gao G (2017) Dynamic cluster formation game for attributed graph clustering. IEEE Trans Cybern 49(1):328–341CrossRef
26.
Wang X, Jin D, Cao X, Yang L, Zhang W (2016) Semantic community identification in large attribute networks, In: Thirtieth AAAI conference on artificial intelligence
27.
Li Z, Pan Z, Hu G, Li G, Zhou X (2017) Detecting semantic communities in social networks. IEICE Trans Fundam Electron Commun Comput Sci 100(11):2507–2512CrossRef
28.
Qin M, Jin D, Lei K, Gabrys B, Musial-Gabrys K (2018) Adaptive community detection incorporating topology and content in social networks. Knowl Based Syst 161:342–356CrossRef
29.
Li Y, Sha C, Huang X, Zhang Y (2018) Community detection in attributed graphs: an embedding approach, In: Thirty-Second AAAI conference on artificial intelligence
30.
Chen H, Xiong Y, Wang C, Zhu Y, Wang W (2020) Spec: sparse embedding-based community detection in attributed graphs, In: International conference on database systems for advanced applications, Springer, pp 53–69
31.
Li R, Ye F, Xie S, Chen C, Zheng Z (2019) Digging into it: community detection via hidden attributes analysis. Neurocomputing 331(2019):97–107
32.
Huang Z, Zhong X, Wang Q, Gong M, Ma X (2020) Detecting community in attributed networks by dynamically exploring node attributes and topological structure. Knowl Based Syst 196:105760CrossRef
33.
34.
Atzmueller M, Günnemann S, Zimmermann A (2021) Mining communities and their descriptions on attributed graphs: a survey. Data Min Knowl Dis 1:1–27MathSciNetMATH
35.
Wang F, Li T, Wang X, Zhu S, Ding C (2011) Community discovery using nonnegative matrix factorization. Data Min Knowl Dis 22(3):493–521MathSciNetCrossRef
36.
Zhang Z-Y (2012) Nonnegative matrix factorization: models, algorithms and applications, Data mining: foundations and intelligent paradigms pp 99–134
37.
Lin C-J (2007) Projected gradient methods for nonnegative matrix factorization. Neural Comput 19(10):2756–2779MathSciNetCrossRef
38.
Kim D, Sra S, Dhillon IS (2007) Fast newton-type methods for the least squares nonnegative matrix approximation problem, In: Proceedings of the Seventh SIAM international conference on data mining, April 26-28, Minneapolis, Minnesota, USA
39.
Zdunek R, Cichocki A (2006) Non-negative matrix factorization with quasi-newton optimization, In: International conference on artificial intelligence and soft computing, Springer, pp 870–879
40.
Pascual-Montano A, Carazo JM, Kochi K, Lehmann D, Pascual-Marqui RD (2006) Nonsmooth nonnegative matrix factorization (nsnmf). IEEE Trans Pattern Anal Mach Intel 28(3):403–415CrossRef
41.
Ding C, Li T, Peng W, Park H (2006) Orthogonal nonnegative matrix t-factorizations for clustering, In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 126–135
42.
Chang Z, Jia C, Yin X, Zheng Y (2019) A generative model for exploring structure regularities in attributed networks. Inform Sci 505:252–264MathSciNetCrossRef
43.
Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110CrossRef
44.
Zhang Z-Y, Wang Y, Ahn Y-Y (2013) Overlapping community detection in complex networks using symmetric binary matrix factorization. Phys Rev E 87(6):062803CrossRef
45.
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks, J Stat Mech 10
46.
Strehl A, Ghosh J (2003) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3(3):583–617MathSciNetMATH
47.
Metadaten
Titel
Community detection combining topology and attribute information
verfasst von
Dan-Dan Lu
Ji Qi
Jie Yan
Zhong-Yuan Zhang
Publikationsdatum
25.01.2022
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2022
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-021-01646-5

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