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 91–100 of 982 papers

TitleStatusHype
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
CCGL: Contrastive Cascade Graph LearningCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Boosting Graph Structure Learning with Dummy NodesCode1
A Large-Scale Database for Graph Representation LearningCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
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Benchmark Results

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