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 161–170 of 982 papers

TitleStatusHype
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
Graph Neural Networks in Recommender Systems: A SurveyCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural NetworksCode1
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
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Benchmark Results

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