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 901–950 of 982 papers

TitleStatusHype
Geometric Scattering Attention NetworksCode0
Robust Causal Graph Representation Learning against Confounding EffectsCode0
Robust Graph Representation Learning via Neural SparsificationCode0
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node ClassificationCode0
MM-GATBT: Enriching Multimodal Representation Using Graph Attention NetworkCode0
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature DistributionCode0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
From ChebNet to ChebGibbsNetCode0
Frameless Graph Knowledge DistillationCode0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs EmbeddingCode0
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagationCode0
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation LearningCode0
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic GraphsCode0
VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network MotifsCode0
Multi-Class and Multi-Task Strategies for Neural Directed Link PredictionCode0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Scalable Graph Compressed ConvolutionsCode0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
Temporal knowledge graph representation learning with local and global evolutionsCode0
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
Conditional Distribution Learning on GraphsCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Multi-Task Graph AutoencodersCode0
Self-supervised Consensus Representation Learning for Attributed GraphCode0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation LearningCode0
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community LabelingCode0
Union Subgraph Neural NetworksCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
EXGC: Bridging Efficiency and Explainability in Graph CondensationCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
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Benchmark Results

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