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 911–920 of 982 papers

TitleStatusHype
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
Dual Graph Representation Learning—0
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version—0
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention—0
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
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
Bridging the Gap between Community and Node Representations: Graph Embedding via Community DetectionCode0
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Benchmark Results

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