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 251–260 of 982 papers

TitleStatusHype
Heterogeneous Deep Graph InfomaxCode0
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation LearningCode0
About Graph Degeneracy, Representation Learning and ScalabilityCode0
Distill2Vec: Dynamic Graph Representation Learning with Knowledge DistillationCode0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
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Benchmark Results

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