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 651–700 of 982 papers

TitleStatusHype
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning—0
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs—0
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network—0
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning—0
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning—0
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
Equivariant Quantum Graph Circuits—0
ETA Prediction with Graph Neural Networks in Google Maps—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
Everything is Connected: Graph Neural Networks—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Explainability in Graph Neural Networks: An Experimental Survey—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review—0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
Federated Graph Representation Learning using Self-Supervision—0
Few-Shot Learning on Graphs—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
FMGNN: Fused Manifold Graph Neural Network—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning—0
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
GraLSP: Graph Neural Networks with Local Structural Patterns—0
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
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Benchmark Results

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