Skip to main navigation
Skip to search
Skip to main content
Illinois Experts Home
LOGIN & Help
Link opens in a new tab
Search content at Illinois Experts
Home
Profiles
Research units
Research & Scholarship
Datasets
Honors
Press/Media
Activities
Generalized Few-Shot Node Classification
Zhe Xu
, Kaize Ding
,
Yu Xiong Wang
, Huan Liu
,
Hanghang Tong
Electrical and Computer Engineering
National Center for Supercomputing Applications (NCSA)
Siebel School of Computing and Data Science
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Generalized Few-Shot Node Classification'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Accuracy Improvement
33%
Adaptive Propagation
33%
Asymmetric Classification
33%
Augmentation Learning
33%
Class Distribution
33%
Data Augmentation
33%
Episodic Training
33%
Experiment Results
33%
Few-shot
100%
Few-shot Learning
66%
Graph Neural Network
33%
Graph Structure
33%
Label Distribution
33%
Learning Perspective
33%
Learning Problems
33%
Learning Scenario
33%
Long Tail
33%
Meta-learning
33%
Node Classification
100%
Novel Class
66%
Real-world Application
33%
Real-world Graphs
33%
Real-world Problems
33%
Test Node
33%
Training Paradigm
33%
Computer Science
Base Class
33%
Class Distribution
33%
Data Augmentation
33%
Few-Shot Learning
66%
Graph Neural Network
33%
Label Distribution
33%
Learning Problem
33%
Meta-Learning
33%
Node Classification
100%
World Application
33%