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 201–250 of 982 papers

TitleStatusHype
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
When Do Flat Minima Optimizers Work?Code1
Personalised Meta-path Generation for Heterogeneous GNNsCode1
Relational Deep Learning: Graph Representation Learning on Relational DatabasesCode1
Evaluating Modules in Graph Contrastive LearningCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
Expander Graph PropagationCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Reward Propagation Using Graph Convolutional NetworksCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
Information Obfuscation of Graph Neural NetworksCode1
SIGN: Scalable Inception Graph Neural NetworksCode1
Simplicial Attention NetworksCode1
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningCode1
Size-Invariant Graph Representations for Graph Classification ExtrapolationsCode1
Disentangle-based Continual Graph Representation LearningCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical UnderstandingCode1
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation LearningCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
FMGNN: Fused Manifold Graph Neural Network—0
Uplifting Message Passing Neural Network with Graph Original Information—0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
DPGNN: Dual-Perception Graph Neural Network for Representation Learning—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—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
Dual Graph Representation Learning—0
Dual Space Graph Contrastive Learning—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Domain Adaptive Graph Classification—0
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Benchmark Results

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