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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 982 papers

TitleStatusHype
Learning node representation via Motif CoarseningCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural NetworksCode0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
Learning to Make Predictions on Graphs with AutoencodersCode0
Learning to Model the Relationship Between Brain Structural and Functional ConnectomesCode0
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation LearningCode0
Representation learning in multiplex graphs: Where and how to fuse information?Code0
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
Understanding microbiome dynamics via interpretable graph representation learningCode0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Residual2Vec: Debiasing graph embedding with random graphsCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Connector 0.5: A unified framework for graph representation learningCode0
LightGCN: Evaluated and EnhancedCode0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation LearningCode0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation LearningCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Graph-based Incident Aggregation for Large-Scale Online Service SystemsCode0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural NetworksCode0
Local2Global: A distributed approach for scaling representation learning on graphsCode0
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Benchmark Results

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