Approximating the Maximum Internal Spanning Tree Problem via a Maximum Path-Cycle Cover. - Xingfu Li; Daming Zhu (ISAAC, 2014)

A 4/3-approximation algorithm for finding a spanning tree to maximize its internal vertices. - Xingfu Li; Daming Zhu (CoRR, 2014)

A Polynomial Time Algorithm for Finding a Spanning Tree with Maximum Number of Internal Vertices on Interval Graphs. - Xingfu Li; Haodi Feng; Haitao Jiang; Binhai Zhu (FAW, 2016)

Construction of Multilayer Porous Scaffold Based on Magnetically Guided Assembly of Microfiber. - Xingfu Li; Huaping Wang; Qing Shi; Tao Sun 001; Qiang Huang 002; Toshio Fukuda (J. Syst. Sci. Complex., 2018)

Solving the maximum internal spanning tree problem on interval graphs in polynomial time. - Xingfu Li; Haodi Feng; Haitao Jiang; Binhai Zhu (Theor. Comput. Sci., 2018)

Connective Steiner 3-eccentricity index and network similarity measure. - Guihai Yu; Xingfu Li (Appl. Math. Comput., 2020)

On the computational complexity of the Steiner k-eccentricity. - Xingfu Li; Guihai Yu; Aleksandar Ilic; Sandi Klavzar (CoRR, 2021)

On the average Steiner 3-eccentricity of trees. - Xingfu Li; Guihai Yu; Sandi Klavzar (Discret. Appl. Math., 2021)

A 43-approximation algorithm for the Maximum Internal Spanning Tree Problem. - Xingfu Li; Daming Zhu; Lusheng Wang 001 (J. Comput. Syst. Sci., 2021)

The Steiner k-eccentricity on trees. - Xingfu Li; Guihai Yu; Sandi Klavzar; Jie Hu; Bo Li (Theor. Comput. Sci., 2021)

The average Steiner 3-eccentricity of block graphs. - Xingfu Li; Guihai Yu (J. Appl. Math. Comput., 2021)

Chinese Comments Classification and Hotspot Mining Algorithm based on Natural Language Processing. - Xingfu Li; Zhongyuan Hua; Huan Jin; Xinyi Lan; Bangchao Wang (ISSSR, 2022)

Topological indices based on 2- or 3-eccentricity to predict anti-HIV activity. - Guihai Yu; Xingfu Li; Deyan He (Appl. Math. Comput., 2022)

The Average Eccentricity of Block Graphs: A Block Order Sequence Perspective. - Xingfu Li; Guihai Yu; Kinkar Chandra Das (Axioms, 2022)

An Empirical Study on Data Balancing in Machine Learning Based Software Traceability Methods. - Bangchao Wang; Zihan Wang; Hongyan Wan; Xingfu Li; Yang Deng (IJCNN, 2023)

Applications of Machine Learning in Requirements Traceability: A Systematic Mapping Study (S). - Xingfu Li; Bangchao Wang; Hongyan Wan; Yang Deng; Zihan Wang (SEKE, 2023)

DF4RT: Deep Forest for Requirements Traceability Recovery Between Use Cases and Source Code. - Bangchao Wang; Yang Deng; Hongyan Wan; Xingfu Li (SMC, 2023)

A Systematic Mapping Study of Machine Learning Techniques Applied to Software Traceability. - Bangchao Wang; Xingfu Li; Hongyan Wan; Yang Deng (SMC, 2023)

MLTracer: An Approach Based on Multi-Layered Gradient Boosting Decision Trees for Requirements Traceability Recovery. - Xingfu Li; Bangchao Wang; Hongyan Wan; Yuanbang Li; Han Jiang; Yang Deng; Zhiyuan Zou (IJCNN, 2024)

An empirical study on the state-of-the-art methods for requirement-to-code traceability link recovery. - Bangchao Wang; Zhiyuan Zou; Hongyan Wan; Yuanbang Li; Yang Deng; Xingfu Li (J. King Saud Univ. Comput. Inf. Sci., 2024)

MTLink: Adaptive multi-task learning based pre-trained language model for traceability link recovery between issues and commits. - Yang Deng; Bangchao Wang; Qiang Zhu; Junping Liu; Jiewen Kuang; Xingfu Li (J. King Saud Univ. Comput. Inf. Sci., 2024)