Dynamic Network Embeddings for Network Evolution Analysis
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Network embeddings learn to represent nodes as low-dimensional vectors to preserve the proximity between nodes and communities of the network for network analysis. The temporal edges (e.g., relationships, contacts, and emails) in dynamic networks are important for network evolution analysis, but few existing methods in network embeddings can capture the dynamic information from temporal edges. In this paper, we propose a novel dynamic network embedding method to analyze evolution patterns of dynamic networks effectively. Our method uses random walk to keep the proximity between nodes and applies dynamic Bernoulli embeddings to train discrete-time network embeddings in the same vector space without alignments to preserve the temporal continuity of stable nodes. We compare our method with several state-of-the-art methods by link prediction and evolving node detection, and the experiments demonstrate that our method generally has better performance in these tasks. Our method is further verified by two real-world dynamic networks via detecting evolving nodes and visualizing their temporal trajectories in the embedded space.
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