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 301–310 of 982 papers

TitleStatusHype
Deep Graph Generators: A Survey—0
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base—0
Deep Feature Learning for Graphs—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach—0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
Graph AI in Medicine—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
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Benchmark Results

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