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 61–70 of 982 papers

TitleStatusHype
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node TasksCode1
Pure Message Passing Can Estimate Common Neighbor for Link PredictionCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based SimilarityCode1
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Neural Architecture RetrievalCode1
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation LearningCode1
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discoveryCode1
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Benchmark Results

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