姓名: 李平

职称: 研究员

院系: 计算机与软件学院

联系方式: pingkly@163.com

       西南石油大学计算机科学学院研究员。四川省学术与技术带头人后备人选，电子科技大学与德国波茨坦大学联合培养博士，香港理工大学博士后，香港城市大学、清华大学、德国马普所访问学者/学生。主要从事图神经网络、自然语言处理、复杂系统及其动力学研究，在人工智能/机器学习顶级会议ICML、EMNLP以及顶级期刊TKDE、TKDD、TVT、TIT、Neural Networks等发表SCI论文60余篇，《中文信息学报》、《中国科学：信息科学》、《情报科学》等中文核心期刊发表论文十余篇，主持及参与国家自然科学基金、省自然科学基金等项目多项，授权国家发明专利30余项。

图挖掘、图深度学习及其应用

[1] Honghao, Wang, Yuan Zhong, Junlei Tang, Kai Zhang*, Ping Li* et al.. “Chain-aware graph neural networks for molecular property prediction”. Bioinformatics, 2024.

[2] Rui Huang, Ping Li*, Kai Zhang. “DPGCL: Dual pass filtering based graph contrastive learning”, Neural Networks, 2024(179),106517.

[3] Huang Jincheng, Ping Li*, Huang Rui, Chen Na, Zhang Acong. Revisiting the Role of Heterophily in Graph Representation Learning: An Edge Classification Perspective[J]. ACM Transactions on Knowledge Discovery from Data（TKDD）, 2024 ,18 (1).

[4] Yu Dai, Junchen Shen, Zijie Zhai, Danlin Liu, Jingyang Chen, Yu Sun, Ping Li, Jie Zhang, Kai Zhang.” High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion”, Forty-First International Conference on Machine Learning (ICML) 2024.

[5] Zijie Zhai, Jingyang Chen, Ping Li, Jie Zhang, Kai Zhang. “Flexible-order Feature-interaction for Mixed Continuous and Discrete Variables with Group-level Interpretability”, ICONIP 2024.

[6] Pinyi Zhang, , Ping Li, Jie Zhang, and Kai Zhang. “Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification”, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing（EMNLP 2024）

[7] Zhang Acong, Huang Jincheng, Li Ping*, Zhang Kai. “Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks,” ACM Transactions on Knowledge Discovery from Data（TKDD）, 2024 March. online.

[8] Li Pan, Li Ping*, Zhang Kai. “Dual-Channel Span for Aspect Sentiment Triplet Extraction,” Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing（EMNLP 2023），2023: 248-261.

[9] Gu Tianyi, Huang Kaiwen, Zhang Jie, Zhang Kai, Li Ping*. “Fast Convolutional Factorization Machine with Enhanced Robustness,” IEEE Transactions on Knowledge and Data Engineering （TKDE）, 2021.

[10] Chen Kaiqi; Yu Lanlan; Zhu Tingting; Li Ping*; Kurths Jurgen. Succinct Representation of Dynamic Networks[J]. IEEE Transactions on Knowledge and Data Engineering（TKDE）, 2021 ,33 (7) :2983-2994.

[11] Wang Yang, Zheng Jin, Du Yuqi, Huang Cheng, Li Ping. “Traffic-GGNN: Predicting Traffic Flow via Attentional Spatial-Temporal Gated Graph Neural Networks,” IEEE Transactions on Intelligent Transportation Systems（TITS）, 2022.

[12] Huang Rui, Li Ping*. “Hub-hub connections matter: Improving edge dropout to relieve over-smoothing in graph neural networks,” Knowledge-Based Systems, 2023 ,270.

[13] Zhu Tingting, Li Ping*, Yu Lanlan, Chen Kaiqi, Chen Yan. “Change point detection in dynamic networks based on community identification，” IEEE Transactions on Network Science and Engineering (TNSE), 2020, 7(3): 2067-2077.

[14] Li Ping, Chen Kaiqi, Ge Yi, Zhang Kai, Small Michael, “Bipartite Centrality Diffusion: Mining higher-order network structures via motif-vertex interactions,” Eur. Phys. Letts.2017, 120(2): 0-28003.

[15] Li Ping, Zhang Kai, Xu Xiaoke, Zhang Jie. “Reexamination of explosive synchronization in scale-free networks: The effect of disassortativity,” Phys. Rev. E, 2013, 87(4): 042803.

[16] Li Ping, Sun Xian, Zhang Kai, Zhang Jie, Small Michael. “Degree-based attacks are not optimal for desynchronization in general networks,” Phys. Rev. E, 2013, 88(2): 022817.