Advertisement

Temporally Evolving Community Detection and Prediction in Content-Centric Networks

  • Ana Paula AppelEmail author
  • Renato L. F. Cunha
  • Charu C. Aggarwal
  • Marcela Megumi Terakado
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)

Abstract

In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks. Such temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurring over multiple snapshots. Incorporating dynamic changes in the communities into the analysis can also provide useful insights about the changes in the network such as the migration of authors across communities. In this work, we propose Chimera (https://github.com/renatolfc/chimera-stf), a shared factorization model that can simultaneously account for graph links, content, and temporal analysis. This approach works by extracting the latent semantic structure of the network in multidimensional form, but in a way that takes into account the temporal continuity of these embeddings. Such an approach simplifies temporal analysis of the underlying network by using the embedding as a surrogate. A consequence of this simplification is that it is also possible to use this temporal sequence of embeddings to predict future communities. We present experimental results illustrating the effectiveness of the approach. Code related to this paper is available at: https://github.com/renatolfc/chimera-stf.

This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

Charu C. Aggarwal’s research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

References

  1. 1.
    Barnes, E.R.: An algorithm for partitioning the nodes of a graph. SIAM J. Algebr. Discret. Methods 3(4), 541–550 (1982).  https://doi.org/10.1137/0603056MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bazzi, M., Porter, M.A., Williams, S., McDonald, M., Fenn, D.J., Howison, S.D.: Community detection in temporal multilayer networks, with an application to correlation networks. Multiscale Model. Simul. 14(1), 1–41 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bergstra, J., Yamins, D., Cox, D.: Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In: International Conference on Machine Learning, pp. 115–123 (2013)Google Scholar
  4. 4.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  5. 5.
    Chen, Y., Kawadia, V., Urgaonkar, R.: Detecting overlapping temporal community structure in time-evolving networks. arXiv preprint arXiv:1303.7226 (2013)
  6. 6.
    Cohn, D., Hofmann, T.: The missing link: a probabilistic model of document content and hypertext connectivity. In: Proceedings of the 13th International Conference on Neural Information Processing Systems, NIPS 2000, pp. 409–415. MIT Press (2000)Google Scholar
  7. 7.
    Cohn, D., Hofmann, T.: The missing link-a probabilistic model of document content and hypertext connectivity. In: Advances in Neural Information Processing Systems, pp. 430–436 (2001)Google Scholar
  8. 8.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gupta, S.K., Phung, D., Adams, B., Tran, T., Venkatesh, S.: Nonnegative shared subspace learning and its application to social media retrieval. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1169–1178. ACM (2010)Google Scholar
  11. 11.
    He, J., Chen, D.: A fast algorithm for community detection in temporal network. Phys. A: Stat. Mech. Appl. 429, 87–94 (2015).  https://doi.org/10.1016/j.physa.2015.02.069CrossRefGoogle Scholar
  12. 12.
    Hofman, J.M., Wiggins, C.H.: Bayesian approach to network modularity. Phys. Rev. Lett. 100(25), 258701 (2008)CrossRefGoogle Scholar
  13. 13.
    İlhan, N., Öğüdücü, Ş.G.: Predicting community evolution based on time series modeling. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM 2015, pp. 1509–1516. ACM (2015).  https://doi.org/10.1145/2808797.2808913
  14. 14.
    İlhan, N., Öğüdücü, Ş.G.: Feature identification for predicting community evolution in dynamic social networks. Eng. Appl. Artif. Intell. 55, 202–218 (2016).  https://doi.org/10.1016/j.engappai.2016.06.003CrossRefGoogle Scholar
  15. 15.
    Kawadia, V., Sreenivasan, S.: Sequential detection of temporal communities by estrangement confinement. Sci. Rep. 2, 794 (2012)CrossRefGoogle Scholar
  16. 16.
    Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 695–704. ACM (2008).  https://doi.org/10.1145/1367497.1367591
  17. 17.
    Lin, Y.R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.L.: FacetNet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th International Conference on World Wide Web, pp. 685–694. ACM (2008)Google Scholar
  18. 18.
    Liu, L., Xu, L., Wangy, Z., Chen, E.: Community detection based on structure and content: a content propagation perspective. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 271–280. IEEE (2015)Google Scholar
  19. 19.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)CrossRefGoogle Scholar
  20. 20.
    Nallapati, R.M., Ahmed, A., Xing, E.P., Cohen, W.W.: Joint latent topic models for text and citations. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 542–550. ACM (2008).  https://doi.org/10.1145/1401890.1401957
  21. 21.
    Nallapati, R.M., Ahmed, A., Xing, E.P., Cohen, W.W.: Joint latent topic models for text and citations. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 542–550. ACM (2008)Google Scholar
  22. 22.
    Pavlopoulou, M.E.G., Tzortzis, G., Vogiatzis, D., Paliouras, G.: Predicting the evolution of communities in social networks using structural and temporal features. In: 2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 40–45 (2017).  https://doi.org/10.1109/SMAP.2017.8022665
  23. 23.
    Pietilänen, A.K., Diot, C.: Dissemination in opportunistic social networks: the role of temporal communities. In: Proceedings of the Thirteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2012, pp. 165–174. ACM (2012).  https://doi.org/10.1145/2248371.2248396
  24. 24.
    Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)CrossRefGoogle Scholar
  25. 25.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)CrossRefGoogle Scholar
  26. 26.
    Ruan, Y., Fuhry, D., Parthasarathy, S.: Efficient community detection in large networks using content and links. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, pp. 1089–1098. ACM (2013).  https://doi.org/10.1145/2488388.2488483
  27. 27.
    Saganowski, S.: Predicting community evolution in social networks. In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 924–925 (2015).  https://doi.org/10.1145/2808797.2809353
  28. 28.
    Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., De Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148–175 (2016)CrossRefGoogle Scholar
  29. 29.
    Takaffoli, M., Rabbany, R., Zaïane, O.R.: Community evolution prediction in dynamic social networks. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 9–16 (2014).  https://doi.org/10.1109/ASONAM.2014.6921553
  30. 30.
    Tang, X., Yang, C.C.: Dynamic community detection with temporal Dirichlet process. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom), pp. 603–608. IEEE (2011)Google Scholar
  31. 31.
    Watts, D.J., Dodds, P.S., Newman, M.E.: Identity and search in social networks. Science 296(5571), 1302–1305 (2002)CrossRefGoogle Scholar
  32. 32.
    Xu, H., Martin, E., Mahidadia, A.: Exploiting paper contents and citation links to identify and characterise specialisations. In: 2014 IEEE International Conference on Data Mining Workshop, pp. 613–620. IEEE (2014)Google Scholar
  33. 33.
    Yang, T., Jin, R., Chi, Y., Zhu, S.: 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, KDD 2009, pp. 927–936. ACM (2009).  https://doi.org/10.1145/1557019.1557120
  34. 34.
    Yu, W., Aggarwal, C.C., Wang, W.: Temporally factorized network modeling for evolutionary network analysis. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, pp. 455–464. ACM (2017).  https://doi.org/10.1145/3018661.3018669
  35. 35.
    Zhou, D., Manavoglu, E., Li, J., Giles, C.L., Zha, H.: Probabilistic models for discovering e-communities. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 173–182. ACM (2006).  https://doi.org/10.1145/1135777.1135807
  36. 36.
    Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2(1), 718–729 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ana Paula Appel
    • 1
    Email author
  • Renato L. F. Cunha
    • 1
  • Charu C. Aggarwal
    • 2
  • Marcela Megumi Terakado
    • 3
  1. 1.IBM ResearchSão PauloBrazil
  2. 2.IBM ResearchYorktownUSA
  3. 3.University of São PauloSão PauloBrazil

Personalised recommendations

Cite paper

Buy options