Dynamic Social Network Analysis Using Link Prediction

Ms Jagtap Tejashree S, Prof. Dr. S. S. Sane


In the domain of social networking, two important and interesting tasks available for analysis. They are namely, Link prediction and Community detection. Link prediction is useful to predict links over time based changes in social behavior of users. Based on the time duration various graph snapshots can be collected over continues streaming data. Temporal Latent space for each user helps the system to represent single user interaction with social network. Global optimization approach is used to infer temporal latent space of each user. The system proposes a framework to predict links for time t+1 based on incremental BCGD algorithm using generated t snapshots of previous social
network analysis and latent spaces of user for previous graph snapshots. The proposed work focuses on a new approach for a new continuous time model that supports continuous inputs rather than discretized graph snapshots.

Full Text:



M. A. Hasan and M. J. Zaki, “A survey of link prediction in social

networks,”in Social Network Data Analytics. New York, NY, USA:

Springer, 2011, pp. 243275.

D. Liben-Nowell andJ. Kleinberg, “The link prediction problem for social

networks,” in Proc. 12th Int. Conf. Inf. Knowl. Manage., 2003, pp.

P. Sarkar and A. W. Moore, “Dynamic social network analysis using latent

space models,” ACM SIGKDD Explorations Newslett., vol. 7, no. 2, pp.

, 2005.

S. Gao, L. Denoyer, and P. Gallinari, “Temporal link prediction by

integrating content and structure information,” in Proc. 20th ACM Int.

Conf. Inf. Knowl. Manage., 2011, pp. 11691174.

J. Ye, H. Cheng, Z. Zhu, and M. Chen, “Predicting positive and negative

links in signed social networks by transfer learning,” in Proc. 22nd Int.

Conf. World Wide Web, 2013, pp. 14771488.

L. Zhu, A. Galstyan, J. Cheng, and K. Lerman, “Tripartite graph clustering for dynamic sentiment analysis on social media,” in Proc. ACM

SIGMOD Int. Conf. Manage. Data, 2014, pp. 15311542

M. W. Berry, M. Browne, A. N. Langville, V. P. Pauca, and R. J.

Plemmons, “Algorithms and applications for approximate nonnegative

matrix factorization,” Comput. Statist. Data Anal., vol. 52, no. 1, pp.

, 2007.

J. Yang and J. Leskovec, “Overlapping community detection at scale: A

nonnegative matrix factorization approach,” in Proc. 6th ACM Int. Conf.

Web Search Data Mining, 2013, pp. 587596.

C. Tantipathananandh and T. Y. Berger-Wolf, “Finding communities in

dynamic social networks,” in Proc. 11th IEEE Int. Conf. Data Mining,

, pp. 12361241.

P. D. Hoff, A. E. Raftery, and M. S. Handcock, “Latent space approaches

to social network analysis,” J. Amer. Statistical Assoc., vol. 97, pp.

, 2002.

M. Mcpherson, L. Smith-Lovin, and J. M. Cook, “Birds of a feather: Homophily in social networks,”Annu. Rev. Sociology, vol. 27, pp. 415444,

L. Zhu, D. Guo, J. Yin, G.V. Steeg, “Scalable Temporal Latent Space

Inferences For Link Prediction in dynamic social Networks,” IEEE

transaction on knowledge and data engineering, vol., 28, no.10, pp. 2765

- 2777,oct-2016.


  • There are currently no refbacks.


Copyright © IJETT, International Journal on Emerging Trends in Technology