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 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
Graph External Attention Enhanced TransformerCode1
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Deep Graph Contrastive Representation LearningCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
Graph Neural Networks in Recommender Systems: A SurveyCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Graph Propagation Transformer for Graph Representation LearningCode1
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
A Gentle Introduction to Deep Learning for GraphsCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
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