SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 31–40 of 982 papers

TitleStatusHype
Deep Graph Contrastive Representation LearningCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Boosting Graph Structure Learning with Dummy NodesCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified