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 301–310 of 982 papers

TitleStatusHype
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature DistributionCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Adaptive Sampling Towards Fast Graph Representation LearningCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
Heterogeneous Deep Graph InfomaxCode0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
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Benchmark Results

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