Kai Guo

According to our database, Kai Guo authored at least 16 papers between 2018 and 2025.

Collaborative distances:

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

2025

From Sequence to Structure: Uncovering Substructure Reasoning in Transformers.

Xinnan Dai, Kai Yang, Jay Revolinsky, Kai Guo, Aoran Wang, Bohang Zhang, Jiliang Tang

CoRR, July, 2025

Empowering GraphRAG with Knowledge Filtering and Integration.

Kai Guo, Harry Shomer, Shenglai Zeng, Haoyu Han, Yu Wang, Jiliang Tang

CoRR, March, 2025

RAG vs. GraphRAG: A Systematic Evaluation and Key Insights.

Haoyu Han, Harry Shomer, Yu Wang, Yongjia Lei, Kai Guo, Zhigang Hua, Bo Long, Hui Liu, Jiliang Tang

CoRR, February, 2025

Retrieval-Augmented Generation with Graphs (GraphRAG).

Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A. Rossi, Subhabrata Mukherjee, Xianfeng Tang, Qi He, Zhigang Hua, Bo Long, Tong Zhao, Neil Shah, Amin Javari, Yinglong Xia, Jiliang Tang

CoRR, January, 2025

Aligning large language models and geometric deep models for protein representation.

Dong Shu, Bingbing Duan, Kai Guo, Kaixiong Zhou, Jiliang Tang, Mengnan Du

Patterns, 2025

Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach.

Shenglai Zeng, Pengfei He, Kai Guo, Tianqi Zheng, Hanqing Lu, Yue Xing, Hui Liu

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024

Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective.

Yang Tao, Kai Guo, Yizhen Zheng, Shirui Pan, Xiaofeng Cao, Yi Chang

Inf. Sci., February, 2024

Exploring the Alignment Landscape: LLMs and Geometric Deep Models in Protein Representation.

Dong Shu, Bingbing Duan, Kai Guo, Kaixiong Zhou, Jiliang Tang, Mengnan Du

CoRR, 2024

Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness.

Kai Guo, Zewen Liu, Zhikai Chen, Hongzhi Wen, Wei Jin, Jiliang Tang, Yi Chang

CoRR, 2024

Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective.

Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang

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

2023

Taming over-smoothing representation on heterophilic graphs.

Kai Guo, Xiaofeng Cao, Zhining Liu, Yi Chang

Inf. Sci., November, 2023

Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships.

Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, Yi Chang

Proceedings of the International Conference on Machine Learning, 2023

2022

Orthogonal Graph Neural Networks.

Kai Guo, Kaixiong Zhou, Xia Hu, Yu Li, Yi Chang, Xin Wang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

IMBENS: Ensemble Class-imbalanced Learning in Python.

Zhining Liu, Zhepei Wei, Erxin Yu, Qiang Huang, Kai Guo, Boyang Yu, Zhaonian Cai, Hangting Ye, Wei Cao, Jiang Bian, Pengfei Wei, Jing Jiang, Yi Chang

CoRR, 2021

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks.

Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang

CoRR, 2021

2018

Cloud-ocean computing: a new scheme of marine data processing.

Jun Liu, Kai Guo, Jun-Hong Cui

Proceedings of the Thirteenth ACM International Conference on Underwater Networks & Systems, 2018