Dynamic Link Prediction
7 papers with code • 2 benchmarks • 3 datasets
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Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes).
We consider a common case in which edges can be short term interactions (e. g., messaging) or long term structural connections (e. g., friendship).