Caihua Shan

According to our database, Caihua Shan authored at least 43 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Chain-of-Model Learning for Language Model.

Kaitao Song, Xiaohua Wang, Xu Tan, Huiqiang Jiang, Chengruidong Zhang, Yongliang Shen, Cen LU, Zihao Li, Zifan Song, Caihua Shan, Yansen Wang, Kan Ren, Xiaoqing Zheng, Tao Qin, Yuqing Yang, Dongsheng Li, Lili Qiu

CoRR, May, 2025

Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning.

Zehui Li, Vallijah Subasri, Yifei Shen, Dongsheng Li, Yiren Zhao, Guy-Bart Stan, Caihua Shan

CoRR, February, 2025

A Comprehensive Analysis on LLM-based Node Classification Algorithms.

Xixi Wu, Yifei Shen, Fangzhou Ge, Caihua Shan, Yizhu Jiao, Xiangguo Sun, Hong Cheng

CoRR, February, 2025

CauseRuDi: Explaining Behavior Sequence Models by Causal Statistics Generation and Rule Distillation.

Yao Zhang, Yun Xiong, Yiheng Sun, Yucheng Jin, Caihua Shan, Tian Lu, Hui Song, Shengli Sun

IEEE Trans. Knowl. Data Eng., January, 2025

How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension.

Xinnan Dai, Haohao Qu, Yifei Shen, Bohang Zhang, Qihao Wen, Wenqi Fan, Dongsheng Li, Jiliang Tang, Caihua Shan

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks (Extended Abstract).

Yao Cheng, Minjie Chen, Caihua Shan, Xiang Li

Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

2024

Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks.

Yao Cheng, Minjie Chen, Caihua Shan, Xiang Li

IEEE Trans. Knowl. Data Eng., December, 2024

Hierarchically Recognizing Vector Graphics and A New Chart-Based Vector Graphics Dataset.

Shuguang Dou, Xinyang Jiang, Lu Liu, Lu Ying, Caihua Shan, Yifei Shen, Xuanyi Dong, Yun Wang, Dongsheng Li, Cairong Zhao

IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension.

Xinnan Dai, Haohao Qu, Yifen Shen, Bohang Zhang, Qihao Wen, Wenqi Fan, Dongsheng Li, Jiliang Tang, Caihua Shan

CoRR, 2024

Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path.

Xinnan Dai, Qihao Wen, Yifei Shen, Hongzhi Wen, Dongsheng Li, Jiliang Tang, Caihua Shan

CoRR, 2024

Can Graph Learning Improve Task Planning?

Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song, Siwei Wang, Bohang Zhang, Jiarui Feng, Hong Cheng, Wei Chen, Yun Xiong, Dongsheng Li

CoRR, 2024

Towards Learning from Graphs with Heterophily: Progress and Future.

Chenghua Gong, Yao Cheng, Jianxiang Yu, Can Xu, Caihua Shan, Siqiang Luo, Xiang Li

CoRR, 2024

Can Graph Learning Improve Planning in LLM-based Agents?

Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song, Siwei Wang, Bohang Zhang, Jiarui Feng, Hong Cheng, Wei Chen, Yun Xiong, Dongsheng Li

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Resurrecting Label Propagation for Graphs with Heterophily and Label Noise.

Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Training-free Multi-objective Diffusion Model for 3D Molecule Generation.

Xu Han, Caihua Shan, Yifei Shen, Can Xu, Han Yang, Xiang Li, Dongsheng Li

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads.

Dingheng Mo, Fanchao Chen, Siqiang Luo, Caihua Shan

Proc. ACM Manag. Data, September, 2023

AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine.

Jie Lian, Xufang Luo, Caihua Shan, Dongqi Han, Varut Vardhanabhuti, Dongsheng Li

CoRR, 2023

Prioritized Propagation in Graph Neural Networks.

Yao Cheng, Minjie Chen, Xiang Li, Caihua Shan, Ming Gao

CoRR, 2023

Label Propagation for Graph Label Noise.

Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li

CoRR, 2023

Biological Factor Regulatory Neural Network.

Xinnan Dai, Caihua Shan, Jie Zheng, Xiaoxiao Li, Dongsheng Li

CoRR, 2023

Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking.

Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li, Ming Gao

CoRR, 2023

SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking.

Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li, Ming Gao

Proceedings of the ACM Web Conference 2023, 2023

CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling.

Yansen Wang, Xinyang Jiang, Kan Ren, Caihua Shan, Xufang Luo, Dongqi Han, Kaitao Song, Yifei Shen, Dongsheng Li

Proceedings of the International Conference on Machine Learning, 2023

Explaining Temporal Graph Models through an Explorer-Navigator Framework.

Wenwen Xia, Mincai Lai, Caihua Shan, Yao Zhang, Xinnan Dai, Xiang Li, Dongsheng Li

Proceedings of the Eleventh International Conference on Learning Representations, 2023

COCLEP: Contrastive Learning-based Semi-Supervised Community Search.

Ling Li, Siqiang Luo, Yuhai Zhao, Caihua Shan, Zhengkui Wang, Lu Qin

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling.

Yuchen Fang, Kan Ren, Caihua Shan, Yifei Shen, You Li, Weinan Zhang, Yong Yu, Dongsheng Li

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

CMMD: Cross-Metric Multi-Dimensional Root Cause Analysis.

Shifu Yan, Caihua Shan, Wenyi Yang, Bixiong Xu, Dongsheng Li, Lili Qiu, Jie Tong, Qi Zhang

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

CLARE: A Semi-supervised Community Detection Algorithm.

Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Caihua Shan, Yiheng Sun, Yangyong Zhu, Philip S. Yu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Finding Global Homophily in Graph Neural Networks When Meeting Heterophily.

Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian

Proceedings of the International Conference on Machine Learning, 2022

RendNet: Unified 2D/3D Recognizer with Latent Space Rendering.

Ruoxi Shi, Xinyang Jiang, Caihua Shan, Yansen Wang, Dongsheng Li

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation.

Yao Zhang, Yun Xiong, Yiheng Sun, Caihua Shan, Tian Lu, Hui Song, Yangyong Zhu

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

On Analyzing Graphs with Motif-Paths.

Xiaodong Li, Reynold Cheng, Kevin Chen-Chuan Chang, Caihua Shan, Chenhao Ma, Hongtai Cao

Proc. VLDB Endow., 2021

Reinforcement Learning Enhanced Explainer for Graph Neural Networks.

Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, Dongsheng Li

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Recognizing Vector Graphics without Rasterization.

Xinyang Jiang, Lu Liu, Caihua Shan, Yifei Shen, Xuanyi Dong, Dongsheng Li

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

CoPE: Modeling Continuous Propagation and Evolution on Interaction Graph.

Yao Zhang, Yun Xiong, Dongsheng Li, Caihua Shan, Kan Ren, Yangyong Zhu

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

How Powerful is Graph Convolution for Recommendation?

Yifei Shen, Yongji Wu, Yao Zhang, Caihua Shan, Jun Zhang, Khaled B. Letaief, Dongsheng Li

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

A Crowdsourcing Framework for Collecting Tabular Data.

Caihua Shan, Nikos Mamoulis, Guoliang Li, Reynold Cheng, Zhipeng Huang, Yudian Zheng

IEEE Trans. Knowl. Data Eng., 2020

CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data.

Xiang Li, Ben Kao, Caihua Shan, Dawei Yin, Martin Ester

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms.

Caihua Shan, Nikos Mamoulis, Reynold Cheng, Guoliang Li, Xiang Li, Yuqiu Qian

Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

A General Early-Stopping Module for Crowdsourced Ranking.

Caihua Shan, Leong Hou U, Nikos Mamoulis, Reynold Cheng, Xiang Li

Proceedings of the Database Systems for Advanced Applications, 2020

A Toolkit for Managing Multiple Crowdsourced Top-K Queries.

Caihua Shan, Leong Hou U, Nikos Mamoulis, Reynold Cheng

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2018

T-Crowd: Effective Crowdsourcing for Tabular Data.

Caihua Shan, Nikos Mamoulis, Guoliang Li, Reynold Cheng, Zhipeng Huang, Yudian Zheng

Proceedings of the 34th IEEE International Conference on Data Engineering, 2018

2017

Truth Inference in Crowdsourcing: Is the Problem Solved?

Yudian Zheng, Guoliang Li, Yuanbing Li, Caihua Shan, Reynold Cheng

Proc. VLDB Endow., 2017