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Found 5 papers, 2 papers with code
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation.
PathMLP: Smooth Path Towards High-order Homophily
However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: 1) over-smoothing due to excessive model depth and propagation times; 2) high-order information is not fully utilized; 3) low computational efficiency.
Clarify Confused Nodes via Separated Learning
A minority of studies attempt to train different node groups separately but suffer from inappropriate separation metrics and low efficiency.
Neighborhood Homophily-based Graph Convolutional Network
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks.
Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction
In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins.
