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 611–620 of 982 papers

TitleStatusHype
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective—0
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships—0
Relational Graph Representation Learning for Open-Domain Question Answering—0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Relation-weighted Link Prediction for Disease Gene Identification—0
Graph Representation Learning in Biomedicine—0
Representation Learning for Spatial Graphs—0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax—0
RESTORE: Graph Embedding Assessment Through Reconstruction—0
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Benchmark Results

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