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Structure-Aware Prototypical Neural Process for Few-Shot Graph Classification 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 15
作者:  Lin, Xixun;  Li, Zhao;  Zhang, Peng;  Liu, Luchen;  Zhou, Chuan;  Wang, Bin;  Tian, Zhihong
收藏  |  浏览/下载：108/0  |  提交时间：2023/02/07
Task analysis  Kernel  Training  Decoding  Stochastic processes  Predictive models  Computational modeling  Few-shot learning  graph classification  graph neural networks (GNNs)  neural process (NP)  
A Comprehensive Survey on Community Detection With Deep Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 21
作者:  Su, Xing;  Xue, Shan;  Liu, Fanzhen;  Wu, Jia;  Yang, Jian;  Zhou, Chuan;  Hu, Wenbin;  Paris, Cecile;  Nepal, Surya;  Jin, Di;  Sheng, Quan Z.;  Yu, Philip S.
收藏  |  浏览/下载：147/0  |  提交时间：2022/04/29
Deep learning  Taxonomy  Optimization  Partitioning algorithms  Clustering algorithms  Social networking (online)  Peer-to-peer computing  Community detection  deep learning  graph neural network  network representation  social networks  
Online Active Learning for Drifting Data Streams 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 15
作者:  Liu, Sanmin;  Xue, Shan;  Wu, Jia;  Zhou, Chuan;  Yang, Jian;  Li, Zhao;  Cao, Jie
收藏  |  浏览/下载：152/0  |  提交时间：2022/04/02
Labeling  Data models  Uncertainty  Biological system modeling  Computational modeling  Cognition  Adaptation models  Active learning  concept drift  data stream classification  online incremental learning  
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 15
作者:  Liu, Yixin;  Li, Zhao;  Pan, Shirui;  Gong, Chen;  Zhou, Chuan;  Karypis, George
收藏  |  浏览/下载：121/0  |  提交时间：2022/04/02
Anomaly detection  Task analysis  Graph neural networks  Unsupervised learning  Predictive models  Pattern matching  Training  Anomaly detection  attributed networks  contrastive self-supervised learning  graph neural networks (GNNs)  unsupervised learning