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 101–125 of 982 papers

TitleStatusHype
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution GeneralizationCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Graph Neural Networks with Adaptive ResidualCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural NetworksCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Graph Trend Filtering Networks for RecommendationsCode1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Certifiably Robust Graph Contrastive LearningCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding AffinityCode1
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