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
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain—0
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data—0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Self-supervised Graph Representation Learning via Bootstrapping—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Self-supervised Learning and Graph Classification under Heterophily—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
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Benchmark Results

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