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 471–480 of 982 papers

TitleStatusHype
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Geometry-Complete Perceptron Networks for 3D Molecular GraphsCode1
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksCode1
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning NetworksCode2
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Implications of sparsity and high triangle density for graph representation learning—0
Federated Graph Representation Learning using Self-Supervision—0
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
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Benchmark Results

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