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Unfolding the Core Structure of the Reciprocal Graph of a Massive Online Social Network

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10043))

Abstract

Google+ (G+ in short) is a directed online social network where nodes have either reciprocal (bidirectional) edges or parasocial (one-way) edges. As reciprocal edges represent strong social ties, we study the core structure of the subgraph formed by them, referred to as the reciprocal network of G+. We develop an effective three-step procedure to hierarchically extract and unfold the core structure of this reciprocal network. This procedure builds up and generalizes ideas from the existing k-shell decomposition and clique percolation approaches, and produces higher-level representations of the core structure of the G+ reciprocal network. Our analysis shows that there are seven subgraphs (“communities”) comprising of dense clusters of cliques lying at the center of the core structure of the G+ reciprocal network, through which other communities of cliques are richly connected. Together they form the core to which “peripheral” sparse subgraphs are attached.

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Notes

  1. 1.

    In this paper we use the terms “user" and “node" interchangeable.

  2. 2.

    It contains more than 90 % of the nodes with at least one reciprocal edge in G+. Hence, our analysis of the dataset is eventually approximate.

References

  1. Gong, N.Z., Xu, W.: Reciprocal versus parasocial relationships in online social networks. Soc. Netw. Anal. Min. 4(1), 184–197 (2014)

    Article  Google Scholar 

  2. Garlaschelli, D., Loffredo, M.I.: Patterns of link reciprocity in directed networks. Phys. Rev. Lett. 93, 268–701 (2004)

    Google Scholar 

  3. Jiang, B., Zhang, Z.-L., Towsley, D.: Reciprocity in social networks with capacity constraints. In: KDD 2015, pp. 457–466. ACM (2015)

    Google Scholar 

  4. Hai, P.H., Shin, H.: Effective clustering of dense and concentrated online communities. In: Asia-Pacific Web Conference (APWEB) 2010, pp. 133–139. IEEE (2010)

    Google Scholar 

  5. Gong, N.Z., Xu, W., Huang, L., Mittal, P., Stefanov, E., Sekar, V., Song, D.: Evolution of the social-attribute networks: measurements, modeling, and implications using Google+. In: IMC 2015, pp. 131–144. ACM (2015)

    Google Scholar 

  6. Gonzalez, R., Cuevas, R., Motamedi, R., Rejaie, R., Cuevas, A.: Google+ or Google–? Dissecting the evolution of the new OSN in its first year. In: WWW 2013, pp. 483–494. ACM (2013)

    Google Scholar 

  7. Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW 2010, pp. 591–600. ACM (2010)

    Google Scholar 

  8. Magno, G., Comarela, G., Saez-Trumper, D., Cha, M., Almeida, V.: New kid on the block: exploring the Google+ social graph. In: IMC 2012, pp. 159–170. ACM (2012)

    Google Scholar 

  9. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: IMC 2007, pp. 29–42. ACM (2007)

    Google Scholar 

  10. Wolfe, A.: Social network analysis: methods and applications. Am. Ethnol. 24(1), 219–220 (1997)

    Article  Google Scholar 

  11. Jamali, M., Haffari, G., Ester, M.: Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns. In: WWW 2011, pp. 527–536. ACM (2011)

    Google Scholar 

  12. Li, Y., Zhang, Z.-L., Bao, J.: Mutual or unrequited love: identifying stable clusters in social networks with uni- and bi-directional links. In: Bonato, A., Janssen, J. (eds.) WAW 2012. LNCS, vol. 7323, pp. 113–125. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30541-2_9

    Chapter  Google Scholar 

  13. Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., Shir, E.: A model of Internet topology using k-shell decomposition. PNAS 104, 11150–11154 (2007)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Google+ Platform. http://www.google.com/intl/en/+/learnmore/

  16. Google+. http://en.wikipedia.org/wiki/Google+

  17. Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  18. Fitting Power Law Distribution. http://tuvalu.santafe.edu/~aaronc/powerlaws/

  19. Cazals, F., Karande, C.: A note on the problem of reporting maximal cliques. Theoret. Comput. Sci. 407(1), 564–568 (2008)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This research was supported in part by DoD ARO MURI Award W911NF-12-1-0385, DTRA grant HDTRA1- 14-1-0040 and NSF grant CNS-1411636. We thank the authors of [6] for the datasets.

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Correspondence to Braulio Dumba .

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Dumba, B., Zhang, ZL. (2016). Unfolding the Core Structure of the Reciprocal Graph of a Massive Online Social Network. In: Chan, TH., Li, M., Wang, L. (eds) Combinatorial Optimization and Applications. COCOA 2016. Lecture Notes in Computer Science(), vol 10043. Springer, Cham. https://doi.org/10.1007/978-3-319-48749-6_58

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  • DOI: https://doi.org/10.1007/978-3-319-48749-6_58

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