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 126–150 of 982 papers

TitleStatusHype
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
Simplicial Attention NetworksCode1
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding AffinityCode1
Multi-modal Graph Learning for Disease PredictionCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
Sign and Basis Invariant Networks for Spectral Graph Representation LearningCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data AugmentationCode1
When Do Flat Minima Optimizers Work?Code1
Molecular Representation Learning via Heterogeneous Motif Graph Neural NetworksCode1
Graph Representation Learning via Aggregation EnhancementCode1
GRPE: Relative Positional Encoding for Graph TransformerCode1
Neural Approximation of Graph Topological FeaturesCode1
How Expressive are Transformers in Spectral Domain for Graphs?Code1
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationCode1
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
RepBin: Constraint-based Graph Representation Learning for Metagenomic BinningCode1
Graph Neural Networks with Adaptive ResidualCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Implicit SVD for Graph Representation LearningCode1
Hierarchical Heterogeneous Graph Representation Learning for Short Text ClassificationCode1
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Benchmark Results

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