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Found 7 papers, 4 papers with code
Second-Order Global Attention Networks for Graph Classification and Regression
Graph Neural Networks (GNNs) are powerful to learn representation of graph-structured data, which fuse both attributive and topological information.
Label-informed Graph Structure Learning for Node Classification
Graph Neural Networks (GNNs) have achieved great success among various domains.
Fully Hyperbolic Graph Convolution Network for Recommendation
These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs.
Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applications across a broad range of domains.
GraphDIVE: Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applications across a broad range of domains.
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation.
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining.
