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 601–625 of 982 papers

TitleStatusHype
Learning Graph Search Heuristics—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Mitigating Relational Bias on Knowledge Graphs—0
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test—0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification—0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
MGTCOM: Community Detection in Multimodal GraphsCode0
Graph representation learning for street networks—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Application of Graph Neural Networks and graph descriptors for graph classification—0
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
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
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Benchmark Results

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