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 501–550 of 982 papers

TitleStatusHype
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Graph Self-Contrast Representation Learning—0
RDGSL: Dynamic Graph Representation Learning with Structure LearningCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
RESTORE: Graph Embedding Assessment Through Reconstruction—0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition—0
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
Local Structure-aware Graph Contrastive Representation Learning—0
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
Graph Contrastive Learning with Generative Adversarial Network—0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
Frameless Graph Knowledge DistillationCode0
Neural Causal Graph Collaborative FilteringCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective—0
Directional diffusion models for graph representation learning—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
Mixed-Curvature Transformers for Graph Representation Learning papersreview—0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Self-supervised Learning and Graph Classification under Heterophily—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Virtual Node Tuning for Few-shot Node Classification—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
Point-Voxel Absorbing Graph Representation Learning for Event Stream based RecognitionCode0
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation—0
Graph-Level Embedding for Time-Evolving Graphs—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Union Subgraph Neural NetworksCode0
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs—0
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention—0
Neural Oscillators are Universal—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
Hierarchical Transformer for Scalable Graph Learning—0
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Benchmark Results

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