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 651–700 of 982 papers

TitleStatusHype
ARIEL: Adversarial Graph Contrastive LearningCode0
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature DistributionCode0
Towards Graph Representation Learning Based Surgical Workflow AnticipationCode0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs—0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Learning node embeddings via summary graphs: a brief theoretical analysis—0
MM-GATBT: Enriching Multimodal Representation Using Graph Attention NetworkCode0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Causal Machine Learning: A Survey and Open Problems—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
MultiSAGE: a multiplex embedding algorithm for inter-layer link prediction—0
Transferable Graph Backdoor Attack—0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation LearningCode0
Learning with Capsules: A Survey—0
A knowledge graph representation learning approach to predict novel kinase-substrate interactionsCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning—0
Embedding Graphs on Grassmann ManifoldCode0
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security PoliciesCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning—0
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning—0
All-optical graph representation learning using integrated diffractive photonic computing units—0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
A Survey on Graph Representation Learning Methods—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Explainability in Graph Neural Networks: An Experimental Survey—0
Few-Shot Learning on Graphs—0
Graph Representation Learning with Individualization and Refinement—0
Graph Representation Learning for Popularity Prediction Problem: A Survey—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Graph Representation Learning Beyond Node and HomophilyCode0
Understanding microbiome dynamics via interpretable graph representation learningCode0
Distribution Preserving Graph Representation Learning—0
Message passing all the way up—0
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning—0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
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Benchmark Results

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