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 851–860 of 982 papers

TitleStatusHype
Learning node representation via Motif CoarseningCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural NetworksCode0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
Learning to Make Predictions on Graphs with AutoencodersCode0
Learning to Model the Relationship Between Brain Structural and Functional ConnectomesCode0
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation LearningCode0
Representation learning in multiplex graphs: Where and how to fuse information?Code0
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Benchmark Results

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