Systematic Dynamic and Heterogeneous Analysis of Rich Social Network Data

1. Balthrop, J., Forrest, S., Newman, M.E.J., Williamson, M.M.: Technological networks and the spread of computer viruses. Science 304(5670), 527–529 (2004)

   Article  Google Scholar 

2. Berger-wolf, T.Y., Saia, J.: A framework for analysis of dynamic social networks. In: Proceedings of ACM KDD, pp. 523–528 (2006)

   Google Scholar 

3. Davis, D., Lichtenwalter, R., Chawla, N.: Multi-relational link prediction in heterogeneous information networks. In: Proceedings of ASONAM, pp. 281–288 (2011)

   Google Scholar 

4. Dong, Y., Tang, J., Wu, S., Tian, J., Chawla, N.V., Rao, J., Cao, H.: Link prediction and recommendation across heterogeneous social networks. In: Proceedings of ICDM, pp. 181–190 (2012)

   Google Scholar 

5. Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal Ubiquitous Computing 10(4), 255–268 (2006)

   Article  Google Scholar 

6. Eagle, N., Pentland, A., Lazer, D.: Inferring friendship network structure by using mobile phone data. PNAS 106(36), 15,274–15,278 (2009)

   Google Scholar 

7. Eagle, N., Pentland, A., Lazer, D.: Inferring friendship network structure by using mobile phone data. PNAS 106(36), 15,274–15,278 (2009)

   Google Scholar 

8. Emmert-Streib, F., Dehmer, M.: Influence of the time scale on the construction of financial networks. PLoS ONE 5(9), e12,884 (2010), doi:10.1371/journal.pone.0012884

   Google Scholar 

9. Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the Internet topology. ACM SIGCOMM Computing Communication Review 29(4), 251–262 (1999)

   Article  Google Scholar 

10. Fortunato, S.: Community detection in graphs. Phys. Rev. E 486, 75–174 (2010)

    MathSciNet  Google Scholar 

11. Greene, D., Doyle, D., Cunningham, P.: Tracking the evolution of communities in dynamic social networks. In: Proceedings of IEEE/ACM ASONAM, pp. 176–183 (2010)

    Google Scholar 

12. Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Uziel, E.: Social media recommendation based on people and tags. In: Proceedings of ACM SIGIR, pp. 194–201 (2010)

    Google Scholar 

13. Hayes, W., Sun, K., Pržulj, N.: Graphlet-based measures are suitable for biological network comparison. Bioinformatics 29(4), 483–491 (2013)

    Article  Google Scholar 

14. Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Link Mining: Models, Algorithms, and Applications, pp. 337–357. Springer (2010)

    Google Scholar 

15. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of ACM SIGKDD, pp. 177–187 (2005)

    Google Scholar 

16. Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., Christakis, N.: Tastes, ties, and time: A new (cultural, multiplex, and longitudinal) social network dataset using facebook.com. Social Networks (2008)

    Google Scholar 

17. Liu, S., Jiang, Y., Striegel, A.: Face-to-face proximity estimation using bluetooth on smartphones. IEEE Transactions on Mobile Computing, 1 (2013)

    Google Scholar 

18. Masuda, N., Holme, P.: Predicting and controlling infectious disease epidemics using temporal networks. F1000Prime Rep. 5, 6 (2013)

    Google Scholar 

19. Mesch, G., Talmud, I.: The quality of online and offline relationships: The role of multiplexity and duration of social relationships. Information Society 22, 137–148 (2006)

    Article  Google Scholar 

20. Milenković, T., Ng, W.L., Hayes, W., Pržulj, N.: Optimal network alignment with graphlet degree vectors. Cancer Informatics 9, 121 (2010)

    Article  Google Scholar 

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

    Google Scholar 

22. Newman, M.: Networks: an introduction. Oxford University Press (2009)

    Google Scholar 

23. Newman, M.E.J.: Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Phys. Rev. E 64, 016,132 (2001)

    Google Scholar 

24. Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)

    Article  Google Scholar 

25. Palla, G., Barabsi, A., Vicsek, T., Hungary, B.: Quantifying social group evolution. Nature 446 (2007)

    Google Scholar 

26. Pržulj, N.: Biological network comparison using graphlet degree distribution. Bioinformatics 23(2), e177–e183 (2007)

    Google Scholar 

27. Solava, R.W., Michaels, R.P., Milenković, T.: Graphlet-based edge clustering reveals pathogen-interacting proteins. Bioinformatics 28(18), i480–i486 (2012)

    Google Scholar 

28. Striegel, A., Liu, S., Meng, L., Poellabauer, C., Hachen, D., Lizardo, O.: Lessons learned from the netsense smartphone study. In: Proceedings of ACM HotPlanet, pp. 51–56 (2013)

    Google Scholar 

29. Sun, Y., Yu, Y., Han, J.: Ranking-based clustering of heterogeneous information networks with star network schema. In: Proceedings of ACM KDD, pp. 797–806 (2009)

    Google Scholar 

30. Wang, X., Sukthankar, G.: Link prediction in multi-relational collaboration networks. In: Proceedings of ACM ASONAM, pp. 1445–1447 (2013)

    Google Scholar 

31. Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P.N., Zhao, B.: User interactions in social networks and their implications. In: Proceedings of ACM EuroSys, pp. 205–218 (2009)

    Google Scholar 

32. Yang, Y., Chawla, N., Sun, Y., Han, J.: Predicting links in multi-relational and heterogeneous networks. In: Proceedings of IEEE ICDM, pp. 755–764 (2012)

    Google Scholar 

33. Ye, J., Chen, K., Wu, T., Li, J., Zhao, Z., Patel, R., Bae, M., Janardan, R., Liu, H., Alexander, G., Reiman, E.: Heterogeneous data fusion for Alzheimers disease study. In: Proceedings of KDD, pp. 1025–1033 (2008)

    Google Scholar 

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