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Found 4 papers, 2 papers with code
Optimality of Message-Passing Architectures for Sparse Graphs
We study the node classification problem on feature-decorated graphs in the sparse setting, i. e., when the expected degree of a node is $O(1)$ in the number of nodes, in the fixed-dimensional asymptotic regime, i. e., the dimension of the feature data is fixed while the number of nodes is large.
Effects of Graph Convolutions in Multi-layer Networks
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information.
Graph Attention Retrospective
They were introduced to allow a node to aggregate information from features of neighbor nodes in a non-uniform way, in contrast to simple graph convolution which does not distinguish the neighbors of a node.
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Recently there has been increased interest in semi-supervised classification in the presence of graphical information.
General Classification
Out-of-Distribution Generalization
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