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Toward Graph Minimally-Supervised Learning
Kaize Ding
, Chuxu Zhang
, Jie Tang
, Nitesh Chawla
,
Huan Liu
Sustainability Initiative
Spatial Reasoning and Policy Analysis, Center for (CSRPA)
Knowledge Enterprise (KE)
Computer Science and Engineering
Assured and Scalable Data Engineering, Center for (CASCADE)
Adaptive Intelligent Materials and Systems Center (AIMS)
Information Assurance Center (IA)
Center for Accelerating Operational Efficiency (CAOE)
Global Futures Laboratory, Julie Ann Wrigley (GFL)
Computing and Augmented Intelligence, School of (IAFSE-SCAI)
Cybersecurity and Digital Forensics, Center for (CDF)
Biodesign Institute
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
9
Scopus citations
Overview
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Dive into the research topics of 'Toward Graph Minimally-Supervised Learning'. Together they form a unique fingerprint.
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Keyphrases
Supervised Learning
100%
Graph Learning
80%
Graph Structure
40%
Labeled Data
40%
Academic Community
20%
State-of-the-art Techniques
20%
Training Model
20%
Real-world Application
20%
Recent Advances
20%
Learning Scenario
20%
Industrial Communities
20%
Learning Methods
20%
New Frontiers
20%
Unsupervised Learning
20%
Human Supervision
20%
Low-resource Settings
20%
Data Graph
20%
Data Labeling
20%
Graph Neural Network
20%
Few-shot Learning
20%
Deep Graph Learning
20%
Data Annotation
20%
Self-supervised Learning Methods
20%
Computer Science
Supervised Learning
100%
Structured Data
33%
Graph Neural Network
33%
World Application
16%
Research Direction
16%
Training Model
16%
Annotation
16%
Few-Shot Learning
16%
Self-Supervised Learning
16%
Neuroscience
Neural Network
100%
Psychology
Neural Network
100%