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 401–410 of 982 papers

TitleStatusHype
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
Deep Learning on Graphs for Natural Language Processing—0
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning—0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Deep Multi-attribute Graph Representation Learning on Protein Structures—0
Graph Neural Networks for Binary Programming—0
Consensus Graph Representation Learning for Better Grounded Image Captioning—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective—0
A Survey on Graph Representation Learning Methods—0
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Benchmark Results

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