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 471–480 of 982 papers

TitleStatusHype
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
Hybrid Low-order and Higher-order Graph Convolutional Networks—0
Hop-Hop Relation-aware Graph Neural Networks—0
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
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Benchmark Results

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