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 151–160 of 982 papers

TitleStatusHype
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Adversarial Graph DisentanglementCode1
GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
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Benchmark Results

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