research-article

Semi-supervised community detection based on non-negative matrix factorization with node popularity

Authors:

Carlo Vittorio CannistraciAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 381, Issue C

Pages 304 - 321

https://doi.org/10.1016/j.ins.2016.11.028

Published: 01 March 2017 Publication History

Abstract

Based on the ideas of graph regularization, we present a semi-supervised and NMF-based model to utilize the prior information of networks.By introducing the parameters of node popularities, we propose a refined PSSNMF model, which is particularly suitable for networks with large degree heterogeneity and unbalanced community structure.The prior information with node popularity being introduced into the model is more effective than being directly encoded into the adjacent matrix. A plethora of exhaustive studies have proved that the community detection merely based on topological information often leads to relatively low accuracy. Several approaches aim to achieve performance improvement by utilizing the background information. But they ignore the effect of node degrees on the availability of prior information. In this paper, by combining the idea of graph regularization with the pairwise constraints, we present a semi-supervised non-negative matrix factorization (SSNMF) model for community detection. And then, to alleviate the influence of the heterogeneity of node degrees and community sizes, we propose an improved SSNMF model by introducing the node popularity, namely PSSNMF, which helps to utilize the prior information more effectively. At last, the extensive experiments on both artificial and real-world networks show that the proposed method improves, as expected, the accuracy of community detection, especially on networks with large degree heterogeneity and unbalanced community structure.

References

[1]

L.A. Adamic, N. Glance, The political blogosphere and the 2004 us election: divided they blog, ACM, 2005.

[2]

V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks, J. Stat. Mech: Theory Exp., 2008 (2008) P10008.

[3]

D. Cai, X. He, J. Han, T.S. Huang, Graph regularized nonnegative matrix factorization for data representation, IEEE Trans. Pattern Anal. Mach. Intell., 33 (2011) 1548-1560.

Digital Library

[4]

C.V. Cannistraci, G. Alanis-Lobato, T. Ravasi, From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks, Sci Rep, 3 (2013).

[5]

K. Chen, J. Lei, Network cross-validation for determining the number of communities in network data, arXiv preprint arXiv:1411.1715 (2014).

[6]

M. Chen, K. Kuzmin, B.K. Szymanski, Community detection via maximization of modularity and its variants, IEEE Trans. Comput. Social Syst., 1 (2014) 46-65.

[7]

P. Chin, A. Rao, V. Vu, Stochastic block model and community detection in sparse graphs: A spectral algorithm with optimal rate of recovery, 2015.

[8]

S. Daminelli, J.M. Thomas, C. Durán, C.V. Cannistraci, Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks, New J. Phys., 17 (2015) 113037.

[9]

L. Danon, A. Diaz-Guilera, J. Duch, A. Arenas, Comparing community structure identification, J. Stat. Mech: Theory Exp., 2005 (2005) P09008.

[10]

X. Deng, Y. Wen, Y. Chen, Highly efficient epidemic spreading model based lpa threshold community detection method, Neurocomputing, 210 (2016) 3-12.

Digital Library

[11]

E. Eaton, R. Mansbach, A spin-glass model for semi-supervised community detection., Citeseer, 2012.

[12]

P. Expert, T.S. Evans, V.D. Blondel, R. Lambiotte, Uncovering space-independent communities in spatial networks, Proc. Nat. Acad. Sci., 108 (2011) 7663-7668.

[13]

S. Fortunato, Community detection in graphs, Phys. Rep., 486 (2010) 75-174.

[14]

S. Fortunato, M. Barthélemy, Resolution limit in community detection, Proc. Nat. Acad. Sci., 104 (2007) 36-41.

[15]

M. Girvan, M.E. Newman, Community structure in social and biological networks, Proc. Nat. Acad. Sci., 99 (2002) 7821-7826.

[16]

R. Guimera, M. Sales-Pardo, L.A.N. Amaral, Modularity from fluctuations in random graphs and complex networks, Phys. Rev. E, 70 (2004) 025101.

[17]

L. Gulikers, M. Lelarge, L. Massoulié, A spectral method for community detection in moderately-sparse degree-corrected stochastic block models, arXiv preprint arXiv:1506.08621 (2015).

[18]

D. He, D. Liu, D. Jin, W. Zhang, A stochastic model for detecting heterogeneous link communities in complex networks, AAAI Press, 2015.

[19]

J.M. Hofman, C.H. Wiggins, Bayesian approach to network modularity, Phys. Rev. Lett., 100 (2008) 258701.

[20]

D. Jin, D. He, Q. Hu, C. Baquero, B. Yang, Extending a configuration model to find communities in complex networks, J. Stat. Mech: Theory Exp., 2013 (2013) P09013.

[21]

R. Kannan, G. Ballard, H. Park, A high-performance parallel algorithm for nonnegative matrix factorization, ACM, 2016.

Digital Library

[22]

B. Karrer, M.E. Newman, Stochastic blockmodels and community structure in networks, Phys. Rev. E, 83 (2011) 016107.

[23]

S. Kataoka, T. Kobayashi, M. Yasuda, K. Tanaka, Community detection algorithm combining stochastic block model and attribute data clustering, J. Phys. Soc. Japan, 85 (2016).

[24]

A. Lancichinetti, S. Fortunato, F. Radicchi, Benchmark graphs for testing community detection algorithms, Phys. Rev. E, 78 (2008) 046110.

[25]

D. Liu, X. Liu, W. Wang, H. Bai, Semi-supervised community detection based on discrete potential theory, Physica A, 416 (2014) 173-182.

[26]

X. Liu, Y.-M. Wei, J. Wang, W.-J. Wang, D.-X. He, Z.-J. Song, Community detection enhancement using non-negative matrix factorization with graph regularization, Int. J. Modern Phys. B (2016) 1650130.

[27]

D. Lusseau, The emergent properties of a dolphin social network, Proc. R. Soc. Lond. B: Biol. Sci., 270 (2003) S186-S188.

[28]

X. Ma, L. Gao, X. Yong, L. Fu, Semi-supervised clustering algorithm for community structure detection in complex networks, Physica A: Stat. Mech. Appl., 389 (2010) 187-197.

[29]

T. Martin, B. Ball, M. Newman, Structural inference for uncertain networks, Phys. Rev. E, 93 (2016) 012306.

[30]

M.E. Newman, Modularity and community structure in networks, Proc. Nat. Acad. Sci., 103 (2006) 8577-8582.

[31]

M.E. Newman, M. Girvan, Finding and evaluating community structure in networks, Phys. Rev. E, 69 (2004) 026113.

[32]

K. Nowicki, T.A.B. Snijders, Estimation and prediction for stochastic blockstructures, J. Am. Stat. Assoc., 96 (2001) 1077-1087.

[33]

M. Rosvall, C.T. Bergstrom, An information-theoretic framework for resolving community structure in complex networks, Proc. Nat. Acad. Sci., 104 (2007) 7327-7331.

[34]

X. Shi, H. Lu, Y. He, S. He, Community detection in social network with pairwisely constrained symmetric non-negative matrix factorization, IEEE, 2015.

[35]

A.L. Traud, P.J. Mucha, M.A. Porter, Social structure of facebook networks, Physica A, 391 (2012) 4165-4180.

[36]

D. Wang, T. Li, S. Zhu, C. Ding, Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization, ACM, 2008.

[37]

F. Wang, T. Li, X. Wang, S. Zhu, C. Ding, Community discovery using nonnegative matrix factorization, Data Min. Knowl. Discov., 22 (2011) 493-521.

Digital Library

[38]

Z. Wang, W. Wang, G. Xue, P. Jiao, X. Li, Semi-supervised community detection framework based on non-negative factorization using individual labels, Springer, 2015.

[39]

J. Xiang, T. Hu, Y. Zhang, K. Hu, J.-M. Li, X.-K. Xu, C.-C. Liu, S. Chen, Local modularity for community detection in complex networks, Physica A: Stat. Mech. Appl., 443 (2016) 451-459.

[40]

J. Xie, S. Kelley, B.K. Szymanski, Overlapping community detection in networks: the state-of-the-art and comparative study, ACM Comput. Surv., 45 (2013) 43.

Digital Library

[41]

L. Yang, D. Jin, X. Wang, X. Cao, Active link selection for efficient semi-supervised community detection, Sci. Rep., 5 (2015).

[42]

W.W. Zachary, An information flow model for conflict and fission in small groups, J. Anthropol. Res. (1977) 452-473.

[43]

P. Zhang, C. Moore, M. Newman, Community detection in networks with unequal groups, Phys. Rev. E, 93 (2016) 012303.

[44]

S. Zhang, H. Zhao, Normalized modularity optimization method for community identification with degree adjustment, Phys. Rev. E, 88 (2013) 052802.

[45]

X. Zhang, M. Newman, Multiway spectral community detection in networks, Phys. Rev. E, 92 (2015) 052808.

[46]

Y. Zhang, D.-Y. Yeung, Overlapping community detection via bounded nonnegative matrix tri-factorization, ACM, 2012.

[47]

Z.-Y. Zhang, Community structure detection in complex networks with partial background information, EPL (Europhys. Lett.), 101 (2013) 48005.

[48]

Z.-Y. Zhang, K.-D. Sun, S.-Q. Wang, Enhanced community structure detection in complex networks with partial background information, Sci. Rep., 3 (2013).

[49]

X. Zhao, X. Li, Z. Zhang, C. Shen, Y. Zhuang, L. Gao, X. Li, Scalable linear visual feature learning via online parallel nonnegative matrix factorization, IEEE Trans. Neural Networks Learn. Syst., 27 (2016) 2628-2642.

[50]

M. Zheng, J. Bu, C. Chen, C. Wang, L. Zhang, G. Qiu, D. Cai, Graph regularized sparse coding for image representation, IEEE Trans. Image Process., 20 (2011) 1327-1336.

Digital Library

Cited By

Ni LGe JZhang YLuo WSheng V(2024)Semi-Supervised Local Community DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329009536:2(823-839)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3290095
Berahmand KMohammadi MSheikhpour RLi YXu Y(2024)WSNMFNeurocomputing10.1016/j.neucom.2023.127041566:COnline publication date: 21-Jan-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127041
Kong QSun JXu Z(2024)Joint orthogonal symmetric non-negative matrix factorization for community detection in attribute networkKnowledge-Based Systems10.1016/j.knosys.2023.111192283:COnline publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111192
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Semi-supervised community detection based on non-negative matrix factorization with node popularity
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 381, Issue C

March 2017

371 pages

ISSN:0020-0255

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Copyright © Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 March 2017

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Cited By

Ni LGe JZhang YLuo WSheng V(2024)Semi-Supervised Local Community DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329009536:2(823-839)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3290095
Berahmand KMohammadi MSheikhpour RLi YXu Y(2024)WSNMFNeurocomputing10.1016/j.neucom.2023.127041566:COnline publication date: 21-Jan-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127041
Kong QSun JXu Z(2024)Joint orthogonal symmetric non-negative matrix factorization for community detection in attribute networkKnowledge-Based Systems10.1016/j.knosys.2023.111192283:COnline publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111192
Zhao YLi WLiu FWang JLuvembe A(2024)Integrating heterogeneous structures and community semantics for unsupervised community detection in heterogeneous networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121821238:PDOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121821
Zou XCao JJu HDing WJin D(2024)GNMFO_TW: Graph Regularized Non-negative Matrix Factorization for Overlapping Community Discovery by Using Three-WayAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5615-5_31(388-398)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5615-5_31
Su SGuan JChen BHuang X(2023)Nonnegative Matrix Factorization Based on Node Centrality for Community DetectionACM Transactions on Knowledge Discovery from Data10.1145/357852017:6(1-21)Online publication date: 28-Feb-2023
https://dl.acm.org/doi/10.1145/3578520
Li YChen GTang YZhang N(2023)Fusion High-Order Information with Nonnegative Matrix Factorization Based Community Infomax for Community DetectionKnowledge Science, Engineering and Management10.1007/978-3-031-40289-0_28(347-359)Online publication date: 16-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-40289-0_28
De Santo AGalli AMoscato VSperlì G(2022)A deep learning approach for semi-supervised community detection in Online Social NetworksKnowledge-Based Systems10.1016/j.knosys.2021.107345229:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.107345
He CZheng YCheng JTang YChen GLiu H(2022)Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoderInformation Sciences: an International Journal10.1016/j.ins.2022.07.036608:C(1464-1479)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.07.036
Jokar EMosleh MKheyrandish M(2022)Discovering community structure in social networks based on the synergy of label propagation and simulated annealingMultimedia Tools and Applications10.1007/s11042-022-12745-y81:15(21449-21470)Online publication date: 15-Mar-2022
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