Abstract: Graph Random Walks (GRWs) offer efficient approximations of key graph properties and have been widely adopted in many applications. However, GRW workloads are notoriously difficult to ...
Abstract: Heterogeneous graph neural networks (HGNNs) are effective for modeling multi-relational structured data. Existing HGNNs usually assume the training samples are relatively sufficient, thus ...
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