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 121–130 of 982 papers

TitleStatusHype
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
Certifiably Robust Graph Contrastive LearningCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
CCGL: Contrastive Cascade Graph LearningCode1
Evaluating Modules in Graph Contrastive LearningCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
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Benchmark Results

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