Graph Neural Network
1474 papers with code • 1 benchmarks • 0 datasets
Most implemented papers
SuperGlue: Learning Feature Matching with Graph Neural Networks
magicleap/SuperGluePretrainedNetwork
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CVPR 2020
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Hierarchical Graph Representation Learning with Differentiable Pooling
dmlc/dgl
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NeurIPS 2018
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
GNNExplainer: Generating Explanations for Graph Neural Networks
RexYing/gnn-model-explainer
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NeurIPS 2019
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
Link Prediction Based on Graph Neural Networks
muhanzhang/SEAL
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NeurIPS 2018
The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs.
Inductive Relation Prediction by Subgraph Reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
Graph Neural Networks for Social Recommendation
These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
DeepSphere: towards an equivariant graph-based spherical CNN
Spherical data is found in many applications.
KGAT: Knowledge Graph Attention Network for Recommendation
xiangwang1223/knowledge_graph_attention_network
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To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.
Few-Shot Learning with Graph Neural Networks
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.
