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Found 3 papers, 2 papers with code
Recurrent Distance Filtering for Graph Representation Learning
Graph neural networks based on iterative one-hop message passing have been shown to struggle in harnessing the information from distant nodes effectively.
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
Specifically, we search for a meta graph, which can capture more complex semantic relations than a meta path, to determine how graph neural networks (GNNs) propagate messages along different types of edges.
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes.
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