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 611–620 of 982 papers

TitleStatusHype
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
GRPE: Relative Positional Encoding for Graph TransformerCode1
Graph Representation Learning via Aggregation EnhancementCode1
SMGRL: Scalable Multi-resolution Graph Representation LearningCode0
Neural Approximation of Graph Topological FeaturesCode1
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
How Expressive are Transformers in Spectral Domain for Graphs?Code1
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning—0
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Benchmark Results

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