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 631–640 of 982 papers

TitleStatusHype
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning—0
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network—0
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
Semi-Supervised Graph Attention Networks for Event Representation LearningCode0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
RepBin: Constraint-based Graph Representation Learning for Metagenomic BinningCode1
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
Learning to Model the Relationship Between Brain Structural and Functional ConnectomesCode0
Knowledge-enhanced Session-based Recommendation with Temporal Transformer—0
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Benchmark Results

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