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 511–520 of 982 papers

TitleStatusHype
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning—0
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations—0
Learning Graph Search Heuristics—0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Learning Hierarchical Graph Representation for Image Manipulation Detection—0
Learning Latent Topology for Graph Matching—0
Learning node embeddings via summary graphs: a brief theoretical analysis—0
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks—0
Learning Robust Representations with Graph Denoising Policy Network—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
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Benchmark Results

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