Recent studies have highlighted the limitations of message-passing based...
On graph data, the multitude of node or edge types gives rise to
heterog...
Graphs can model complex relationships between objects, enabling a myria...
Conventional graph neural networks (GNNs) are often confronted with fair...
Subgraph isomorphism counting is an important problem on graphs, as many...
Demand estimation plays an important role in dynamic pricing where the
o...
Unbiased learning to rank (ULTR) aims to train an unbiased ranking model...
Graph neural networks (GNNs) emerge as a powerful family of representati...
Semi-supervised node classification on graphs is an important research
p...
In this paper, we study the problem of using representation learning to
...