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 101–110 of 982 papers

TitleStatusHype
Deep Graph Contrastive Representation LearningCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Graph External Attention Enhanced TransformerCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Multi-hop Attention Graph Neural NetworkCode1
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution GeneralizationCode1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
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Benchmark Results

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