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 901–910 of 982 papers

TitleStatusHype
MAGNET: Multi-Label Text Classification using Attention-based Graph Neural NetworkCode1
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version—0
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention—0
Graph Representation Learning via Graphical Mutual Information MaximizationCode1
A Survey on Knowledge Graphs: Representation, Acquisition and ApplicationsCode2
Towards Graph Representation Learning in Emergent Communication—0
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised ClassificationCode0
Graph Ordering: Towards the Optimal by Learning—0
Robust Graph Representation Learning via Neural SparsificationCode0
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Benchmark Results

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