已发表或录用的第一作者及通讯作者论文：
2024
[1] Tong Lin, Cangqi Zhou*, Hui Chen, Qianmu Li, Dianming Hu. Multi-level Disentangled Contrastive Learning on Heterogeneous Graphs. The 16th International Conference on Machine Learning and Computing (ICMLC 2024), Shenzhen, China. February 2-5, 2024. Forthcoming.
[2] Cangqi Zhou, Hui Chen, Jing Zhang*, Qianmu Li, Dianming Hu. Quintuple-based Representation Learning for Bipartite Heterogeneous Networks. ACM Transactions on Intelligent Systems and Technology (TIST). Forthcoming.
2023
[1] Yingjie Xie, Qi Yan, Cangqi Zhou*, Jing Zhang, Dianming Hu. Heterogeneous Graph Contrastive Learning with Dual Aggregation Scheme and Adaptive Augmentation. The 7th International Joint Conference on Web and Big Data (APWeb-WAIM-2023), Wuhan, China. October 6-8, 2023. Forthcoming.
[2] Peishuo Liu, Cangqi Zhou*, Xiao Liu, Jing Zhang, Qianmu Li. Multi-Granularity Contrastive Learning for Graph with Hierarchical Pooling. The 32nd International Conference on Artificial Neural Networks (ICANN-2023), Heraklion city, Crete, Greece. September 26-29, 2023.
[3] Peishuo Liu, Canggi Zhou*, Jing Zhang, Qianmu li, and Dianming Hu. Hierarchical Graph ContrastiveLearning via Debiasing Noise Samples with Adaptive Repelling Ratio. The 23rd IEEE International Conferenceon Data Mining (ICDM-2023), Shanghai, China. December 1-4, 2023.
2022
[1] Chen Hou, Cangqi Zhou*, Chu-Ge Wu, Rui Cong, Kun Li. Optimization of Cloud-Based Multi- Agent System for Trade-Off Between Trustworthiness of Data and Cost of Data Usage. IEEE Transactions on Automation Science and Engineering (T-ASE), Early Access. Nov. 30, 2022.
[2] Chen Hou, Cangqi Zhou*, Chu-ge Wu, Rui Cong, Kun Li. Optimal Model of Cloud-Based Multi-Agent System for Trade-off Between Trustworthiness of Data and Cost of Data Usage. IEEE 18th International Conference on Automation Science and Engineering (CASE-2022), Mexico City, Mexico. Aug. 20-24, 2022.
[3] Cangqi Zhou, Yuxiang Wang, Jing Zhang*, Jiqiong Jiang, Dianming Hu. End-to-end modularity-based community co-partition in bipartite networks. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM-2022), pp. 2711-2720, Atlanta, GA, USA. Oct. 17-21, 2022.
[4] Yuxiang Wang, Cangqi Zhou*, Jing Zhang, Qianmu Li. Attributed graph clustering with double contrastive projector. In Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN-2022), Padua, Italy. Jul. 18-23, 2022.
[5] Cangqi Zhou, Jing Zhang*, Kaisheng Gao, Qianmu Li, Dianming Hu, Victor S. Sheng. Bipartite network embedding with symmetric neighborhood convolution. Expert Systems with Applications, 198:116757. Jul. 2022.
[6] Cangqi Zhou, Sunyue Xu, Hao Ban, Jing Zhang*. Neural topic modeling with Gaussian mixture model and householder flow. In Proceedings of 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2022), Chengdu, China. May. 16-19, 2022.
[7] Cangqi Zhou, Hui Chen, Jing Zhang*, Qianmu Li, Dianming Hu. AngHNE: Representation learning for bipartite heterogeneous networks with angular loss. In Proceedings of the 15th International Conference on Web Search and Data Mining (WSDM-2022), pp. 1470–1478, Phoenix, Arizona, USA. Feb. 21-25, 2022.
2021
[1] Cangqi Zhou, Jinling Shang, Jing Zhang*, Qianmu Li, Dianming Hu. Topic-attentive encoder-decoder with pre-trained language model for keyphrase generation. In Proceedings of the 21st International Conference on Data Mining (ICDM-2021), pp. 1529–1534, Auckland, New Zealand. Dec. 7-10, 2021.
[2] Jing Zhang*, Mengxi Li, Kaisheng Gao, Shunmei Meng, Cangqi Zhou*. Word and graph attention networks for semi-supervised classification. Knowledge and Information Systems (KAIS), 63:2841–2859. Nov. 2021.
[3] Cangqi Zhou, Hui Chen, Jing Zhang*, Qianmu Li, Dianming Hu, Victor S. Sheng. Multi-label graph node classification with label attentive neighborhood convolution. Expert Systems with Applications, 180:115063. Oct. 2021.
[4] Hui Chen, Cangqi Zhou*, Jing Zhang, Qianmu Li. Heterogeneous graph embedding based on edge-aware neighborhood convolution. In Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN-2021), Virtual. Jul. 18-22, 2021.
Before 2020
[1] Cangqi Zhou, Hao Ban*, Jing Zhang, Qianmu Li, Yinghua Zhang*. Gaussian mixture variational autoencoder for semi-supervised topic modeling. IEEE Access, 8:106843–106854. Jun. 2020.
[2] Cangqi Zhou, Liang Feng, Qianchuan Zhao*. A novel community detection method in bipartite networks. Physica A: Statistical Mechanics and its Applications, 492:1679-1693. Feb. 2018.
[3] Cangqi Zhou, Qianchuan Zhao, Wenbo Lu. Cumulative dynamics of independent information spreading behaviour: A physical perspective. Scientific Reports, 7:5530. Jul. 2017.
[4] Cangqi Zhou, Qianchuan Zhao, Wenbo Lu. Impact of repeated exposures on information spreading in social networks. PLoS ONE, 10(10):e0140556. Oct. 2015.
[5] Cangqi Zhou, Qianchuan Zhao, Wenbo Lu. Modeling of the forwarding behavior in microblogging with adaptive interest. (in Chinese). Journal of Tsinghua University (Science and Technology), 55(11):1163-1170. Dec. 2015.
[6] Cangqi Zhou, Qianchuan Zhao. Efficient time series clustering and its application to social network mining. Journal of Intelligent Systems, 23(2):213-229. Feb. 2014.
[7] Cangqi Zhou, Qianchuan Zhao, Ruixi Yuan. An angle-based dissimilarity for accelerating the clustering of dynamic data in networks. 10th IEEE International Conference on Networking, Sensing and Control (ICNSC-2013), Evry, France. Apr. 10-12, 2013.