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

Showing 5160 of 982 papers

TitleStatusHype
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Multi-hop Attention Graph Neural NetworkCode1
Disentangle-based Continual Graph Representation LearningCode1
Certifiably Robust Graph Contrastive LearningCode1
Data Augmentation on Graphs: A Technical SurveyCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Adversarial Graph DisentanglementCode1
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Benchmark Results

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