Dynamic Social Network Analysis Using Link Prediction

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

Abstract


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.

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