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
MAGNET: Multi-Label Text Classification using Attention-based Graph Neural NetworkCode1
Graph Contrastive Learning with Adaptive AugmentationCode1
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on GraphsCode1
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
GCondenser: Benchmarking Graph CondensationCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
CCGL: Contrastive Cascade Graph LearningCode1
A Representation Learning Framework for Property GraphsCode1
Information Obfuscation of Graph Neural NetworksCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
Certifiably Robust Graph Contrastive LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Graph External Attention Enhanced TransformerCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
A step towards neural genome assemblyCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
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Benchmark Results

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