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 851–900 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
Local2Global: Scaling global representation learning on graphs via local trainingCode0
Local Distance-Preserving Node Embeddings and Their Performance on Random GraphsCode0
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired DatasetCode0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
LGIN: Defining an Approximately Powerful Hyperbolic GNNCode0
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment DecodingCode0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
Gossip and Attend: Context-Sensitive Graph Representation LearningCode0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule MiningCode0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Massively Parallel Graph Drawing and Representation LearningCode0
Material Prediction for Design Automation Using Graph Representation LearningCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph RepresentationsCode0
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
Memory-based Message Passing: Decoupling the Message for Propogation from DiscriminationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
GL-Coarsener: A Graph representation learning framework to construct coarse grid hierarchy for AMG solversCode0
MGTCOM: Community Detection in Multimodal GraphsCode0
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised ClassificationCode0
CommunityGAN: Community Detection with Generative Adversarial NetsCode0
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Benchmark Results

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