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 926–950 of 982 papers

TitleStatusHype
GraLSP: Graph Neural Networks with Local Structural Patterns—0
Graph Transformer for Graph-to-Sequence LearningCode0
Graph Representation Learning via Multi-task Knowledge Distillation—0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?Code0
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
Fundamental Limits of Deep Graph Convolutional Networks—0
Graph Representation learning for Audio & Music genre Classification—0
Decoupling feature propagation from the design of graph auto-encoders—0
Relational Graph Representation Learning for Open-Domain Question Answering—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
On the Interpretability and Evaluation of Graph Representation Learning—0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
Learning Robust Representations with Graph Denoising Policy Network—0
Universal Graph Transformer Self-Attention NetworksCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Towards Interpretable Molecular Graph Representation Learning—0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Adaptive Graph Representation Learning for Video Person Re-identificationCode0
Graph Representation Learning: A SurveyCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
ChainNet: Learning on Blockchain Graphs with Topological Features—0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation LearningCode0
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Benchmark Results

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