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 61–70 of 982 papers

TitleStatusHype
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Optimizing Supply Chain Networks with the Power of Graph Neural Networks—0
KAN KAN Buff Signed Graph Neural Networks?—0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
LASE: Learned Adjacency Spectral EmbeddingsCode0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation LearningCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain—0
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Benchmark Results

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