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 771–780 of 982 papers

TitleStatusHype
Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach—0
Implications of sparsity and high triangle density for graph representation learning—0
Improving Graph-Based Text Representations with Character and Word Level N-grams—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning—0
Inductive Graph Representation Learning with Quantum Graph Neural Networks—0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
Inferential SIR-GN: Scalable Graph Representation Learning—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Information propagation dynamics in Deep Graph Networks—0
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Benchmark Results

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