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Online social network analysis: detection of communities of interest

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Abstract

The second generation of World Wide Web, supplied with the platforms of Social Networks, proves to be a revealing source of knowledge. It has led to the emergence of online communities of interest. In fact, the interactions of users through web usages allow the exchange of information and the dissemination of innovation; thereby the formation of cohesive groups of individuals sharing goals, interests, semantics and services. Reflection on the evolution of those groups and their detection is a fundamental topic of interest in the field of Social Network Analysis. This problem is very challenging and hard to solve despite the huge interdisciplinary research over the past years. In this paper, we will attempt an indepth comparative review of the proposed approaches for clustering Social Network actors into communities of interests and propose a new classification of these approaches.

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References

  • Agarwal, G., & Kempe, D. (2008). Modularity-maximizing graph communities via mathematical programming. The European Physical Journal B, 66, 409–418.

    MathSciNet  MATH  Google Scholar 

  • Alves, N.A. (2007). Unveiling community structures in weighted networks. Physical Review E, 76, 036101.

    Google Scholar 

  • Armstrong, A., & Hagel, J. (1996). The real value of on-line communities. Harvard Business Review, 134–141.

  • Barabasi, A.L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.

    MathSciNet  MATH  Google Scholar 

  • Barbieri, N., Bonchi, F., Manco, G. (2013). Cascade-based community detection. In 6th ACM international conference on web search and data mining (pp. 33–42).

  • Berry, J., Hendrickson, B., LaViolette, R., Phillips, C. (2011). Tolerating the community detection resolution limit with edge weighting. Physical Review E, 83, 056119.

    Google Scholar 

  • Blei, D.M., Griffiths, T., Jordan, M. (2010). The nested Chinese restaurant process and Bayesian non- parametric inference of topic hierarchies. Journal of the ACM, 57(2), 7:1–7:30.

    MATH  Google Scholar 

  • Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics.

  • Boccaletti, S., Ivanchenko, M., Pluchino, A., Latora, V., Rapisarda, A. (2007). Detecting complex network modularity by dynamical clustering. Physical Review E, 75(R), 045102.

    Google Scholar 

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

    Google Scholar 

  • Brandes, U., Lerner, J., Lubbers, M.J., McCarty, C., Molina, J.L., Nagel, U. (2010). Recognizing modes of acculturation in personal networks of migrants. Proc. 6th Conf. Applications of Social Network Analysis (ASNA 2009), Procedia - Social Behavioral and Sciences, 4, 4–13.

    Google Scholar 

  • Brun, C., Chevenet, F., Martin, D., Wojcik, J., Guenoche, A., Jacq, B. (2003). Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol., 5, R6.

    Google Scholar 

  • Chen, W.Y., Dress, A.W.M., Winking, Q.Y. (2008). Community structures of networks. Mathematics in Computer Science, 1, 441–457.

    MathSciNet  MATH  Google Scholar 

  • Christensen, I., Schiaffino, S., Armentano, M. (2016). Social group recommendation in the tourism domain. Journal of Intelligent Information Systems, 47, 1–23.

    Google Scholar 

  • Clauset, A. (2005). Finding local community structure in networks. Physical Review E, 72, 026132.

    Google Scholar 

  • Clauset, A., Newman, M.E.J., Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111.

    Google Scholar 

  • Combe, D. (2013). Dtection de communauts dans les rseaux d’information utilisant liens et attributs. Intelligence artificielle [cs.AI]. Universite Jean Monnet - Saint-Etienne.

  • Combe, D., Largeron, C., Egyed-Zsigmond, E., Gery, M. (2012). Getting clusters from structure data and attribute data. In IEEE/ACM international conference on advances in social networks analysis and mining (pp. 710–712).

  • Crandall, D.J., Cosley, D., Huttenlocher, D.P., Kleinberg, J.M., Suri, S. (2008). Feedback effects between similarity and social in uence in online communities. In Li, Y., Liu, B., Sarawagi, S. (Eds.) KDD, ACM (pp. 160–168).

  • Darlay, J., Brauner, N., Nadia, J. (2010). Moncel, Partition en sous graphes denses pour la détection de communautés, ROADEF 11ème congrès de la société française de Recherche opérationnelle et d’Aide à la décision France.

  • Degenne, A., & Forsé, M. (2004). Les réseaux sociaux. Mathematics and Social Sciences, 42, 5–9.

    MATH  Google Scholar 

  • Ding, Y. (2011). Community detection: topological vs topical. Journal of Informetrics, 5(4), 498–514.

    Google Scholar 

  • Donetti, L., & Munoz, M.A. (2004). Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics, 10, P10012.

    MATH  Google Scholar 

  • Ereteo, G., Gandon, F., Buffa, M. (2011). Semtagp: semantic community detection in folksonomies. In IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology (Vol. 1, pp. 324–331).

  • Faloutsos, M., Faloutsos, P., Faloutsos, C. (1999). On power-law relationships of the internet topology. In SIGCOMM (pp. 251–262).

    MATH  Google Scholar 

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

    Google Scholar 

  • Fortunato, S. (2009). Community detection in graphs. Physics Reports, 486(3-5), 103.

    MathSciNet  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  • Hastings, M.B. (2006). Community detection as an inference problem. Physical Review E, 74(R), 035102.

    Google Scholar 

  • Heimo, T., Kumpula, J., Kaski, K., Saramaki, J. (2008). Detecting modules in dense weighted networks with the Potts method. Journal of Statistical Mechanics: Theory and experiment P08007.

    Google Scholar 

  • Hu, Y., Chen, H., Zhang, P., Li, M., Di, Z., Fan, Y. (2008). Comparative definition of community and corresponding identifying algorithm. Physical Review E, 78(2), 026121.

    Google Scholar 

  • Kernighan, B.W., & Lin, S. (1970). An efficient heuristic procedure for partitioning graphs. The Bell System Technical Journal, 49, 291–307.

    MATH  Google Scholar 

  • Li, H., Nie, Z., Lee, Z.W., Giles, C., Wen, J. (2008). Scalable community discovery on textual data with relations. In Proceedings of the conference on information and knowledge management.

  • Li, D., Leyva, I., Almendral, J.A., Sendina-Nadal, I., Buldu, J.M., Havlin, S., Boccaletti, S. (2008). Synchronization interfaces and overlapping communities in complex networks. Physical Review Letters, 101, 168701.

    Google Scholar 

  • Liben-Nowell, D., & Kleinberg, J. (2003). The link prediction problem for social networks. In Conference on information and knowledge management.

  • Lytras, M.D., Zhuhadar, L., Zhang, J.X., Kurilovas, E. (2014). Advances of scientific research on technology enhanced learning in Social Networks and mobile contexts: towards high effective educational platforms for next generation education. Journal of Universal Computer Science, 20, 1402–1406.

    Google Scholar 

  • Lytras, M.D., Mathkour, H.I., Abdalla, H., Yanez-Marquez, C., Pablos, P.O. (2014). The social media in academia and education research Revolutions and a paradox : Advanced next generation social learning innovation. Journal of Universal Computer Science, 20, 1987–1994.

    Google Scholar 

  • Ma, H. (2011). Internet of things: objectives and scientific challenges. Journal of Computer Science and Technology, 26, 919–924.

    Google Scholar 

  • Middleton, A.A., & Fisher, D.S. (2002). Three-dimensional random-field Ising magnet: interfaces, scaling, and the nature of states. Physical Review B, 65, 134411.

    Google Scholar 

  • Natarajan, N., Sen, P., Chaoji, V. (2013). Community detection in content-sharing social networks. In Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining (pp. 82–89).

  • Nepusz, T., Petroczi, A., Negyessy, L., Bazso, F. (2008). Fuzzy communities and the concept of bridgeness in complex networks. Physical Review E, 77, 016107.

    MathSciNet  Google Scholar 

  • Newman, M.E.J. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69, 066133.

    Google Scholar 

  • Newman, M.E. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74, 036104.

    MathSciNet  Google Scholar 

  • Newman, M.E.J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 026113.

    Google Scholar 

  • Newman, M.E.J., & Leicht, E.A. (2007). Mixture models and exploratory analysis in networks. Proceedings of the National Academy of Sciences of the Unites States, 104, 9564.

    MATH  Google Scholar 

  • Palla, G., Derenyi, I., Farkas, I., Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks. Nature, 435, 814–818.

    Google Scholar 

  • Papadopoulos, S., Kompatsiaris, Y., Vakali, A., Spyridonos, P. (2012). Community detection in Social Media. Data Mining and Knowledge Discovery, 24(3), 515–554.

    Google Scholar 

  • Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. Proceedings of the 20th International Symposium on Computer and Information Sciences, 3733, 284–293.

    MATH  Google Scholar 

  • Recalde, L., Nettleton, D.F., Baeza-Yates, R., Boratto, L. (2017). Detection of trending topic communities: Bridging content creators and distributors. In FDIA: BCS, workshops in computing.

  • Reddy, P.K., Kitsuregawa, M., Sreekanth, P., Rao, S.S. (2002). A graph based approach to extract a neighborhood customer community for collaborative filtering. In Databases in networked information systems (pp. 188–200).

  • Reichardt, J., & Bornholdt, S. (2004). Detecting fuzzy community structures in complex networks with a Potts model. Physical Review, 93, 218701.

    Google Scholar 

  • Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74(1), 016110.

    MathSciNet  Google Scholar 

  • Reichardt, J., & White, D.R. (2007). Role models for complex networks. European Physical Journal B, 60, 217–224.

    MATH  Google Scholar 

  • Rosvall, M., & Bergstrom, C.T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences of the United States of America, 105, 1118–1123.

    Google Scholar 

  • Shen, C.W., & Chu, S.H. (2014). Web 2.0 and social networking services in municipal emergency management: a study of U.S. cities. Journal of Universal Computer Science, 20, 1995–2004.

    Google Scholar 

  • Simmel, G. (1917). Sociologie et épistémologie, Paris, PUF 1981 1ère édn.

  • Simonsen, I. (2005). Diffusion and networks: a powerful combination! Physica A: Statistical Mechan-ics and its Applications, 357, 317–330.

    Google Scholar 

  • Stegehuis, C., Remco, D.H., Johan, S.H. (2016). Epidemic spreading on complex networks with community structure. Scientific Reports, 2829–2838.

  • Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z. (2008). Arnetminer: extraction and mining of academic social networks. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 990–998).

  • Trevor, H., Robert, T., Jerome, F. (2001). The elements of statistical learning. Springer.

  • Valverde-Rebaza, J.C., & Lopes, A.A. (2012). Link Prediction in complex networks based on cluster information. In Lecture notes in computer science.

  • Xiang, R., Neville, J., Rogati, M. (2010). Modeling relationship strength in online social networks. In Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (Eds.) WWW (p./pp. 981-990), : ACM ISBN: 978-1-60558-799-8.

  • Xu, G., Tsoka, S., Papageorgiou, L.G. (2007). Finding community structures in complex networks using mixed integer optimization. The European Physical Journal B, 60, 231–239.

    MATH  Google Scholar 

  • Xu, Z., Ke, Y., Wang, Y., Cheng, H., Cheng, J. (2012). A model-based approach to attributed graph clustering. In 12th proceedings of the ACM SIGMOD international conference on management of data (pp. 505–516).

  • Yang, B., & Liu, J. (2008). Discovering global network communities based on local centralities. ACM Transactions on the Web, 2(1), 1–32.

    MathSciNet  Google Scholar 

  • Yang, B., Liu, D., Liu, J. (2010). Discovering communities from social networks: methodologies and applications. The Handbook of Social Networks: Technologies and Applications, 2, 331–346.

    Google Scholar 

  • Yang, J., McAuley, J., Leskovec, J. (2013). J Community detection in networks with node attributes. In IEEE 13th international conference on data mining (pp. 1151–1156).

  • Zhou, H. (1908). Network landscape from a Brownian particle’s perspective. Physical Review E, 67(04), 2003.

    Google Scholar 

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

    Google Scholar 

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Correspondence to Nadia Chouchani.

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Chouchani, N., Abed, M. Online social network analysis: detection of communities of interest. J Intell Inf Syst 54, 5–21 (2020). https://doi.org/10.1007/s10844-018-0522-7

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