( Image credit: Hierarchical Graph Pooling with Structure Learning )
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Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.
Ranked #2 on Graph Classification on D&D
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
Ranked #2 on Graph Classification on REDDIT-B
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
Ranked #1 on Node Classification on Cora
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Ranked #1 on Graph Classification on IPC-lifted
Therefore, how to ﬁnd a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.
Ranked #2 on Graph Classification on BP-fMRI-97
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
Ranked #1 on Node Classification on AIFB
Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
Ranked #2 on Graph Classification on IPC-grounded
The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.
Ranked #3 on Graph Classification on RE-M5K
In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales.