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 351–400 of 982 papers

TitleStatusHype
Frameless Graph Knowledge DistillationCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?Code0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Complete and Efficient Graph Transformers for Crystal Material Property PredictionCode0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
CommunityGAN: Community Detection with Generative Adversarial NetsCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
Graph-based Incident Aggregation for Large-Scale Online Service SystemsCode0
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic GraphsCode0
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Calibrating and Improving Graph Contrastive LearningCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Graph Contrastive Learning for Connectome ClassificationCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Hyperbolic Neural NetworksCode0
Multi-Task Graph AutoencodersCode0
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural NetworksCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
EXGC: Bridging Efficiency and Explainability in Graph CondensationCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Graphine: A Dataset for Graph-aware Terminology Definition GenerationCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community LabelingCode0
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Benchmark Results

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