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cs.LG2021
FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows
Effrosyni Simou
In several machine learning tasks for graph structured data, the graphs under consideration may be composed of a varying number of nodes. Therefore, it is necessary to design pooli…
cs.LG2020
node2coords: Graph Representation Learning with Wasserstein Barycenters
Effrosyni Simou, Dorina Thanou, Pascal Frossard
In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods do not learn rep…