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 151–175 of 982 papers

TitleStatusHype
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
GRAPE for Fast and Scalable Graph Processing and random walk-based EmbeddingCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Deep Graph Contrastive Representation LearningCode1
Graph External Attention Enhanced TransformerCode1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution GeneralizationCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural NetworksCode1
Graph Propagation Transformer for Graph Representation LearningCode1
A Gentle Introduction to Deep Learning for GraphsCode1
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
Graph Trend Filtering Networks for RecommendationsCode1
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge FeaturesCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
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Benchmark Results

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