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

Showing 271280 of 982 papers

TitleStatusHype
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Heterogeneous Deep Graph InfomaxCode0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
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Benchmark Results

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