GTEA: Representation Learning for Temporal Interaction Graphs via Edge Aggregation

We consider the problem of representation learning for temporal interaction graphs where a network of entities with complex interactions over an extended period of time is modeled as a graph with a rich set of node and edge attributes. In particular, an edge between a node-pair within the graph corresponds to a multi-dimensional time-series... (read more)

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