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Deep Graph Learning: Foundations, Advances and Applications
Yu Rong
, Tingyang Xu
, Junzhou Huang
, Wenbing Huang
, Hong Cheng
, Yao Ma
, Yiqi Wang
, Tyler Derr
, Lingfei Wu
, Tengfei Ma
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
36
Scopus citations
Overview
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Dive into the research topics of 'Deep Graph Learning: Foundations, Advances and Applications'. Together they form a unique fingerprint.
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Keyphrases
Deep Graph Learning
100%
Neural Network Model
71%
Graph Neural Network
57%
Drug Discovery
14%
Machine Learning
14%
Social Networks
14%
Graph Representation
14%
Adaptation
14%
Deep Learning
14%
Computer Vision
14%
Machine Data
14%
Natural Language Processing
14%
Cross-domain
14%
Learning Objectives
14%
Graph Classification
14%
Link Prediction
14%
Social Network Analysis
14%
Medical Image Analysis
14%
Unsupervised Learning
14%
Node Graph
14%
Feature Engineering
14%
Self-supervised Learning
14%
Deep Graph Neural Network
14%
Mining Communities
14%
Gridless Method
14%
Computer Science
Graph Neural Network
100%
Neural Network Model
41%
Data Mining
8%
Social Network
8%
Computer Vision
8%
Machine Learning
8%
Deep Learning
8%
Image Analysis
8%
Natural Language Processing
8%
Link Prediction
8%
Social Network Analysis
8%
Unsupervised Learning
8%
Self-Supervised Learning
8%
Biochemistry, Genetics and Molecular Biology
Social Network
100%
Natural Language Processing
50%
Neuroscience
Neural Network
100%
Natural Language Processing
20%