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Node-wise Diffusion for Scalable Graph Learning
Keke Huang
, Jing Tang
, Juncheng Liu
,
Renchi Yang
, Xiaokui Xiao
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
25
Citations (Scopus)
Overview
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Dive into the research topics of 'Node-wise Diffusion for Scalable Graph Learning'. Together they form a unique fingerprint.
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Keyphrases
Semi-supervised Learning
100%
Node Representation
100%
Scalable Graph Neural Network
100%
Web Services
50%
Training Model
50%
Online Social Networks
50%
Unique Characteristics
50%
Electronic Commerce
50%
Diffusion Model
50%
Order of Magnitude
50%
Superior Performance
50%
Sampling Methods
50%
Local Context
50%
Complete Representation
50%
Page Analysis
50%
Web Application
50%
F1 Score
50%
Social Recommendation
50%
Graph Neural Network
50%
Neighbor Sampling
50%
Web Dataset
50%
Semi-supervised Setting
50%
Service Analysis
50%
Message Passing Model
50%
Computer Science
Semisupervised Learning
100%
Experimental Result
50%
Online Social Network
50%
Web Service
50%
Diffusion Model
50%
Superior Performance
50%
Web Application
50%
Graph Neural Network
50%
Sampling Technique
50%
Message Passing
50%