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 401–450 of 982 papers

TitleStatusHype
Learning node representation via Motif CoarseningCode0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
EXGC: Bridging Efficiency and Explainability in Graph CondensationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?Code0
PROXI: Challenging the GNNs for Link PredictionCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community LabelingCode0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
Querying functional and structural niches on spatial transcriptomics dataCode0
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation LearningCode0
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Calibrating and Improving Graph Contrastive LearningCode0
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance ModelCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Graph Representation Learning for Contention and Interference Management in Wireless NetworksCode0
Hyperbolic Neural NetworksCode0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
ARIEL: Adversarial Graph Contrastive LearningCode0
Cell Attention NetworksCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Embedding Graphs on Grassmann ManifoldCode0
Robust Graph Representation Learning via Neural SparsificationCode0
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming EventsCode0
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
Neural Causal Graph Collaborative FilteringCode0
Conditional Distribution Learning on GraphsCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Graph Representation Learning via Ladder Gamma Variational AutoencodersCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Heterogeneous Deep Graph InfomaxCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
Show:102550
← PrevPage 9 of 20Next →

Benchmark Results

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