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 491500 of 982 papers

TitleStatusHype
SGA: A Graph Augmentation Method for Signed Graph Neural Networks0
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems0
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks0
DINE: Dimensional Interpretability of Node EmbeddingsCode0
Transformers are efficient hierarchical chemical graph learnersCode0
Learning node representation via Motif CoarseningCode0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
Graph Representation Learning Towards Patents Network Analysis0
Deep Prompt Tuning for Graph Transformers0
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Benchmark Results

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