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 961970 of 982 papers

TitleStatusHype
Embedding Graphs on Grassmann ManifoldCode0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality EstimationCode0
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent SamplingCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Normed Spaces for Graph EmbeddingCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Cycle Representation Learning for Inductive Relation PredictionCode0
Semi-Supervised Graph Attention Networks for Event Representation LearningCode0
OLGA: One-cLass Graph AutoencoderCode0
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Benchmark Results

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