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Network embeddings learn to represent nodes as low-dimensional vectors topreserve the proximity between nodes and communities of the network for networkanalysis. The temporal edges (e.g., relationships, contacts, and emails) indynamic networks are important for network evolution analysis, but few existingmethods in network embeddings can capture the dynamic information from temporaledges. In this paper, we propose a novel dynamic network embedding method toanalyze evolution patterns of dynamic networks effectively. Our method usesrandom walk to keep the proximity between nodes and applies dynamic Bernoulliembeddings to train discrete-time network embeddings in the same vector spacewithout alignments to preserve the temporal continuity of stable nodes. Wecompare our method with several state-of-the-art methods by link prediction andevolving node detection, and the experiments demonstrate that our methodgenerally has better performance in these tasks. Our method is further verifiedby two real-world dynamic networks via detecting evolving nodes and visualizingtheir temporal trajectories in the embedded space.

Dynamic Network Embeddings for Network Evolution Analysis