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 11–20 of 982 papers

TitleStatusHype
Simple yet Effective Graph Distillation via Clustering—0
Supervised Graph Contrastive Learning for Gene Regulatory Network—0
Convexified Message-Passing Graph Neural Networks—0
Mini-Game Lifetime Value Prediction in WeChat—0
LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural NetworksCode0
Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease DetectionCode0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Large Language Model Enhancers for Graph Neural Networks: An Analysis from the Perspective of Causal Mechanism Identification—0
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic—0
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelsCode1
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Benchmark Results

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