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 21–30 of 982 papers

TitleStatusHype
Boosting Graph Structure Learning with Dummy NodesCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
A Large-Scale Database for Graph Representation LearningCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
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Benchmark Results

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