Top

Soft Computing

Published in:

30-10-2018 | Methodologies and Application

A novel granular approach for detecting dynamic online communities in social network

Authors: Hamideh Sadat Cheraghchi, Ali Zakerolhosseini, Saeed Bagheri Shouraki, Elaheh Homayounvala

Published in: Soft Computing | Issue 20/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The great surge in the research of community discovery in complex network is going on due to its challenging aspects. Dynamicity and overlapping nature are among the common characteristics of these networks which are the main focus of this paper. In this research, we attempt to approximate the granular human-inspired viewpoints of the networks. This is especially helpful when making decisions with partial knowledge. In line with the principle of granular computing, in which precision is avoided, we define the micro- and macrogranules in two levels of nodes and communities, respectively. The proposed algorithm takes microgranules as input and outputs meaningful communities in rough macrocommunity form. For this purpose, the microgranules are drawn toward each other based on a new rough similarity measure defined in this paper. As a result, the structure of communities is revealed and adapted over time, according to the interactions observed in the network, and the number of communities is extracted automatically. The proposed model can deal with both the low and the sharp changes in the network. The algorithm is evaluated in multiple dynamic datasets and the results confirm the superiority of the proposed algorithm in various measures and scenarios.

previous article Failure modes and effects analysis for CO2 transmission pipelines using a hesitant fuzzy VIKOR method

next article An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Ahn Y-Y, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466(7307):761–764CrossRef

Amelio A, Pizzuti C (2014) Overlapping community discovery methods: a survey. In: Gündüz-Öğüdücü Ş, Etaner-Uyar A (eds) Social networks: analysis and case studies. Lecture notes in social networks. Springer, Vienna, pp 105–125

Becker E, Robisson B, Chapple CE, Guénoche A, Brun C (2011) Multifunctional proteins revealed by overlapping clustering in protein interaction network. Bioinformatics 28(1):84–90CrossRef

Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:P10008MATHCrossRef

Breve F, Zhao L (2013) Fuzzy community structure detection by particle competition and cooperation. Soft Comput 17(4):659–673CrossRef

Cazabet R, Amblard F (2014) Dynamic community detection. In: Alhajj R, Rokne J (eds) Encyclopedia of social network analysis and mining. Springer, New York, NY, pp 404–414. https://doi.org/10.1007/978-1-4614-6170-8_383 CrossRef

Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 554–560

Chi Y, Song X, Zhou D, Hino K, Tseng BL (2007) Evolutionary spectral clustering by incorporating temporal smoothness. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 153–162

Dillen NB, Chakraborty A (2016) Modularity-based community detection in fuzzy granular social networks. In: Proceedings of the international congress on information and communication technology. Springer, pp 577–585

Ding Z, Zhang X, Sun D, Luo B (2016) Overlapping community detection based on network decomposition. Sci Rep 6:24115CrossRef

Fahmi A, Abdullah S, Amin F, Ali A (2018) Weighted average rating (War) method for solving group decision making problem using triangular cubic fuzzy hybrid aggregation (Tcfha). Punjab Univ J Math 50(1):23–34MathSciNet

Folino F, Pizzuti C (2014) An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans Knowl Data Eng 26(8):1838–1852CrossRef

Görke R, Maillard P, Schumm A, Staudt C, Wagner D (2013) Dynamic graph clustering combining modularity and smoothness. J Exp Algorithmics (JEA) 18(1):1–5MathSciNetMATH

Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: 2010 international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 176–183

Grossberg S (2013) Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Netw 37:1–47CrossRef

Gupta S, Kumar P, Bhasker B (2016) A rough connectedness algorithm for mining communities in complex networks. In: International conference on big data analytics and knowledge discovery. Springer, pp 34–48

Hartmann T, Kappes A, Wagner D (2016) Clustering evolving networks. In: Kliemann L, Sanders P (eds) Algorithm engineering. Springer, Cham, pp 280–329CrossRef

Kundu S, Pal SK (2015) FGSN: fuzzy granular social networks-model and applications. Inf Sci 314:100–117CrossRef

Lin Y-R, Chi Y, Zhu S, Sundaram H, Tseng BL (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 685–694

Lin Y-R, Chi Y, Zhu S, Sundaram H, Tseng BL (2009) Analyzing communities and their evolutions in dynamic social networks. ACM Trans Knowl Discov Data (TKDD) 3(2):8

Lingras P, West C (2004) Interval set clustering of web users with rough k-means. J Intell Inf Syst 23(1):5–16MATHCrossRef

Liu H, Liu C, C-a Wu (2015) A framework of granular computing clustering algorithms. Int J Hybrid Inf Technol 8(12):225–230CrossRef

Lusseau D, Schneider K, Boisseau OJ, Haase P, Slooten E, Dawson SM (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54(4):396–405CrossRef

McDaid AF, Greene D, Hurley N (2011) Normalized mutual information to evaluate overlapping community finding algorithms. arXiv preprint arXiv:11102515

Nguyen NP, Dinh TN, Tokala S, Thai MT (2011) Overlapping communities in dynamic networks: their detection and mobile applications. In: Proceedings of the 17th annual international conference on Mobile computing and networking. ACM, pp 85–96

Oner SC, Oztaysi B (2018) An interval type 2 hesitant fuzzy MCDM approach and a fuzzy c means clustering for retailer clustering. Soft Comput 22:4971–4987CrossRef

Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818CrossRef

Peters G, Weber R (2009) Intelligent cluster algorithms for changing data structures. Int J Intell Defence Support Syst 2(2):105–119CrossRef

Peters G, Weber R (2016) DCC: a framework for dynamic granular clustering. Granul Comput 1(1):1–11CrossRef

Peters G, Crespo F, Lingras P, Weber R (2013) Soft clustering–fuzzy and rough approaches and their extensions and derivatives. Int J Approx Reason 54(2):307–322MathSciNetCrossRef

Plantié M, Crampes M (2013) Survey on social community detection. In: Ramzan N, van Zwol R, Lee J-S, Clüver K, Hua X-S (eds) Social media retrieval. Springer, London, pp 65–85CrossRef

Rosvall M, Bergstrom CT (2010) Mapping change in large networks. PLoS ONE 5(1):e8694CrossRef

Sawyer RK (2005) Social emergence: societies as complex systems. Cambridge University Press, CambridgeCrossRef

Steinhaeuser K, Chawla NV (2008) Community detection in a large real-world social network. In: Liu H, Salerno JJ, Young MJ (eds) Social computing, behavioral modeling, and prediction. Springer, Boston, pp 168–175CrossRef

Steinhaeuser K, Chawla NV (2010) Identifying and evaluating community structure in complex networks. Pattern Recognit Lett 31(5):413–421CrossRef

Takaffoli M, Fagnan J, Sangi F, Zaïane OR (2011) Tracking changes in dynamic information networks. In: 2011 international conference on computational aspects of social networks (CASoN) IEEE, pp 94–101

Tang L, Liu H, Zhang J (2012) Identifying evolving groups in dynamic multimode networks. IEEE Trans Knowl Data Eng 24(1):72–85CrossRef

Wang W, Liu D, Liu X, Pan L (2013) Fuzzy overlapping community detection based on local random walk and multidimensional scaling. Physica A 392(24):6578–6586CrossRef

Whang JJ, Gleich DF, Dhillon IS (2016) Overlapping community detection using neighborhood-inflated seed expansion. IEEE Trans Knowl Data Eng 28(5):1272–1284CrossRef

Xie J, Szymanski BK, Liu X (2011) Slpa: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 IEEE 11th international conference on data mining workshops. IEEE, pp 344–349

Xie J, Chen M, Szymanski BK (2013) LabelrankT: Incremental community detection in dynamic networks via label propagation. Proceedings of the workshop on dynamic networks management and mining. ACM

Xie J, Kelley S, Szymanski BK (2013b) Overlapping community detection in networks: the state of the art and comparative study. ACM Comput Surv 45(4):43MATHCrossRef

Xu KS, Kliger M, Hero Iii AO (2014) Adaptive evolutionary clustering. Data Min Knowl Disc 28(2):304–336MathSciNetMATHCrossRef

Yager RR, Filev D (1998) Operations for granular computing: mixing words and numbers. In: The 1998 IEEE international conference on fuzzy systems proceedings. IEEE World Congress on Computational Intelligence. IEEE, pp 123–128

Yang T, Chi Y, Zhu S, Gong Y, Jin R (2011) Detecting communities and their evolutions in dynamic social networks—a Bayesian approach. Mach Learn 82(2):157–189MathSciNetMATHCrossRef

Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473CrossRef

Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127MathSciNetMATHCrossRef

Title: A novel granular approach for detecting dynamic online communities in social network
Authors: Hamideh Sadat Cheraghchi
Ali Zakerolhosseini
Saeed Bagheri Shouraki
Elaheh Homayounvala
Publication date: 30-10-2018
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 20/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3585-z

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 20/2019

Fuzzy clustering approach for brain tumor tissue segmentation in magnetic resonance images

Granular autoencoders: concepts and design

River flow prediction using hybrid PSOGSA algorithm based on feed-forward neural network

Using max-continuous T fuzzy relation equations to determine the vector

A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation

The maximum points-based supervised learning rule for spiking neural networks

Premium Partner