Leveraging similarity and community-based features for link prediction in temporal social networks using attention-enabled LSTM

Link prediction refers to predicting future or missing links within the social network. Static social networks, with their fixed topology, require less data analysis, making predicting future or missing links relatively easier than in dynamic social networks. Dynamic social networks change over time, which makes link prediction challenging. Modern similarity-based features look at the topological structure of the network but only use a single snapshot to calculate, so they miss important patterns and changes in the network over time. Community features, which divide the whole network into communities, help to improve the prediction by focusing on links between the intra-communities. Our proposed framework, SimCom-AN-LSTM, shows that combining structural and community features can leverage the strengths of both approaches. The sequence-based Long Short-Term Memory (LSTM) model processes the snapshot sequences to capture the network's temporal dependencies. These features help the model learn temporal patterns and predict future links by capturing local interaction between the nodes, the overall network topology, and community structure. The attention mechanism enhances the performance of the LSTM model by giving more weight to the feature that yields the best results. Results indicate that the proposed framework surpasses the individual state-of-the-art algorithms.