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Cycling topic graph learning for neural topic modeling Journal article
Liu, Yanyan, Gong, Zhiguo. Cycling topic graph learning for neural topic modeling[J]. Knowledge-Based Systems, 2025, 310.
Authors:  Liu, Yanyan;  Gong, Zhiguo
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.2/7.4 | Submit date：2025/01/22
Neural Topic Model  Graph Neural Networks  Wasserstein Autoencoder  Graph Attention Networks  
Feature aggregation and connectivity for object re-identification Journal article
Han, Dongchen, Liu, Baodi, Shao, Shuai, Liu, Weifeng, Zhou, Yicong. Feature aggregation and connectivity for object re-identification[J]. Pattern Recognition, 2025, 157, 110869.
Authors:  Han, Dongchen;  Liu, Baodi;  Shao, Shuai;  Liu, Weifeng;  Zhou, Yicong
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.5/7.6 | Submit date：2025/01/22
Object Re-identification  Graph Convolutional Networks  Feature Aggregation  
GraKerformer: A Transformer With Graph Kernel for Unsupervised Graph Representation Learning Journal article
Xu, Lixiang, Liu, Haifeng, Yuan, Xin, Chen, Enhong, Tang, Yuanyan. GraKerformer: A Transformer With Graph Kernel for Unsupervised Graph Representation Learning[J]. IEEE Transactions on Cybernetics, 2024, 54(12), 7320-7332.
Authors:  Xu, Lixiang;  Liu, Haifeng;  Yuan, Xin;  Chen, Enhong;  Tang, Yuanyan
Favorite | TC[WOS]:1 TC[Scopus]:2  IF:9.4/10.3 | Submit date：2024/12/05
Graph Kernel  Graph Neural Networks (Gnns)  Structural Encoding Method  Transformer  
Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation Conference paper
Cai, Jinyu, Zhang, Yunhe, Lu, Zhoumin, Guo, Wenzhong, Ng, See Kiong. Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation[C]:Association for Computing Machinery, Inc, 2024, 5537-5546.
Authors:  Cai, Jinyu;  Zhang, Yunhe;  Lu, Zhoumin;  Guo, Wenzhong;  Ng, See Kiong
Favorite | TC[Scopus]:1 | Submit date：2024/12/05
Anomaly Detection  Federated Learning  Graph Neural Networks  Unsupervised Learning  
From Cluster Assumption to Graph Convolution: Graph-Based Semi-Supervised Learning Revisited Journal article
Wang, Zheng, Ding, Hongming, Pan, Li, Li, Jianhua, Gong, Zhiguo, Yu, Philip S.. From Cluster Assumption to Graph Convolution: Graph-Based Semi-Supervised Learning Revisited[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024.
Authors:  Wang, Zheng;  Ding, Hongming;  Pan, Li;  Li, Jianhua;  Gong, Zhiguo; et al.
Favorite | TC[WOS]:2 TC[Scopus]:3  IF:10.2/10.4 | Submit date：2024/11/05
Data Mining  Graph Convolutional Neural Networks  Graph-based Semi-supervised Learning (Gssl)  
Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials Journal article
Zhao, Zirui, Li, Hai Feng. Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials[J]. ACS Applied Materials & Interfaces, 2024, 16(39), 53153-53162.
Authors:  Zhao, Zirui;  Li, Hai Feng
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:8.3/8.7 | Submit date：2024/10/10
Graph Neural Networks (Gnns)  Interface Diffusion  Material Properties Prediction  Atomic Structure Modeling  Semiconductor Interfaces  
Predicting doping strategies for ternary nickel–cobalt–manganese cathode materials to enhance battery performance using graph neural networks Journal article
Zhao, Zirui, Luo, Dong, Wu, Shuxing, Sun, Kaitong, Lin, Zhan, Li, Hai Feng. Predicting doping strategies for ternary nickel–cobalt–manganese cathode materials to enhance battery performance using graph neural networks[J]. Journal of Energy Storage, 2024, 98, 112982.
Authors:  Zhao, Zirui;  Luo, Dong;  Wu, Shuxing;  Sun, Kaitong;  Lin, Zhan; et al.
Favorite | TC[WOS]:3 TC[Scopus]:3  IF:8.9/9.0 | Submit date：2024/08/05
Doping Strategies  Electrochemical Performance  Graph Neural Networks  Lithium-ion Batteries  Ternary Nickel–cobalt–manganese Cathode Materials  
Brain Network Manifold Learned by Cognition-Inspired Graph Embedding Model for Emotion Recognition Journal article
Li, Cunbo, Li, Peiyang, Chen, Zhaojin, Yang, Lei, Li, Fali, Wan, Feng, Cao, Zehong, Yao, Dezhong, Lu, Bao Liang, Xu, Peng. Brain Network Manifold Learned by Cognition-Inspired Graph Embedding Model for Emotion Recognition[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2024.
Authors:  Li, Cunbo;  Li, Peiyang;  Chen, Zhaojin;  Yang, Lei;  Li, Fali; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:8.6/8.7 | Submit date：2024/10/10
Affective Brain-computer Interface (Abci) System  Cognition-inspired Learning  Electroencephalogram (Eeg) Brain Networks  Emotion Recognition  Geometry Manifold  Graph Embedding  L1-norm Space  
DyGKT: Dynamic Graph Learning for Knowledge Tracing Conference paper
KE CHENG, LINZHI PENG, PENGYANG WANG, JUNCHEN YE, LEILEI SUN, BOWEN DU. DyGKT: Dynamic Graph Learning for Knowledge Tracing[C], New York, NY, USA:Association for Computing Machinery, 2024, 409-420.
Authors:  KE CHENG;  LINZHI PENG;  PENGYANG WANG;  JUNCHEN YE;  LEILEI SUN; et al.
Favorite | TC[Scopus]:1 | Submit date：2024/08/28
Dynamic Graph  Educational Data Mining  Graph Neural Networks  Knowledge Tracing  
Generalized Few-Shot Node Classification With Graph Knowledge Distillation Journal article
Wang, Jialong, Zhou, Mengting, Zhang, Shilong, Gong, Zhiguo. Generalized Few-Shot Node Classification With Graph Knowledge Distillation[J]. IEEE Transactions on Computational Social Systems, 2024.
Authors:  Wang, Jialong;  Zhou, Mengting;  Zhang, Shilong;  Gong, Zhiguo
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:4.5/4.6 | Submit date：2024/05/16
Few-shot Learning (Fsl)  Graph Neural Networks (Gnns)  Knowledge Distillation  Node Classification