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 551–560 of 982 papers

TitleStatusHype
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
A Survey on Malware Detection with Graph Representation Learning—0
A Survey on Spectral Graph Neural Networks—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities—0
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
Asymmetric Graph Representation Learning—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
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Benchmark Results

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