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Found 2 papers, 1 papers with code
TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation
We argue that when learning high-order information from temporal graphs, we encounter two challenges, i. e., computational inefficiency and over-smoothing, that cannot be solved by conventional techniques applied on static graphs.
Heterogeneous Graph Contrastive Multi-view Learning
How to mitigate the sampling bias for heterogeneous GCL is another important problem.
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