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
Metric Based Few-Shot Graph ClassificationCode1
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group DiscriminationCode1
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
Relphormer: Relational Graph Transformer for Knowledge Graph RepresentationsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
Simplicial Attention NetworksCode1
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding AffinityCode1
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Benchmark Results

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