Lingjing Kong

752 total citations
24 papers, 145 citations indexed

About

Lingjing Kong is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Lingjing Kong has authored 24 papers receiving a total of 145 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 8 papers in Computer Networks and Communications and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Lingjing Kong's work include Internet Traffic Analysis and Secure E-voting (4 papers), Network Security and Intrusion Detection (4 papers) and Peer-to-Peer Network Technologies (3 papers). Lingjing Kong is often cited by papers focused on Internet Traffic Analysis and Secure E-voting (4 papers), Network Security and Intrusion Detection (4 papers) and Peer-to-Peer Network Technologies (3 papers). Lingjing Kong collaborates with scholars based in China, United States and Switzerland. Lingjing Kong's co-authors include Guowei Huang, Fei Mi, Boi Faltings, Yiqiao Cai, Ziyan Wu, Shaopeng Liu, Tao Lin, Ying Zhou, Kaicheng Yu and Minlie Huang and has published in prestigious journals such as Information Sciences, Applied Soft Computing and International Immunopharmacology.

In The Last Decade

Lingjing Kong

21 papers receiving 141 citations

Peers

Lingjing Kong
Yiqun Lisa Yin China
Didier Chételat Canada
Lei Cui China
Alejandro Arbeláez Ireland
Jiale Guo China
Qiao Zhang China
Krzysztof Maziarz United Kingdom
Yunming Ye China
Yiqun Lisa Yin China
Lingjing Kong
Citations per year, relative to Lingjing Kong Lingjing Kong (= 1×) peers Yiqun Lisa Yin

Countries citing papers authored by Lingjing Kong

Since Specialization
Citations

This map shows the geographic impact of Lingjing Kong's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lingjing Kong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lingjing Kong more than expected).

Fields of papers citing papers by Lingjing Kong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lingjing Kong. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lingjing Kong. The network helps show where Lingjing Kong may publish in the future.

Co-authorship network of co-authors of Lingjing Kong

This figure shows the co-authorship network connecting the top 25 collaborators of Lingjing Kong. A scholar is included among the top collaborators of Lingjing Kong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lingjing Kong. Lingjing Kong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ma, Xuehui, Yongli Wang, Lingjing Kong, et al.. (2025). FZHWT alleviates chronic atrophic gastritis by inhibiting inflammatory pathways and promoting mucosal repair. International Immunopharmacology. 153. 114473–114473. 1 indexed citations
2.
Zhou, Ying, Lingjing Kong, & Hui Wang. (2025). A multiobjective edge-based learning algorithm for the vehicle routing problem with time windows. Information Sciences. 715. 122223–122223. 1 indexed citations
3.
Xie, Shaoan, Lingjing Kong, Yujia Zheng, et al.. (2025). SmartCLIP: Modular Vision-language Alignment with Identification Guarantees. 29780–29780.
4.
Zhou, Ying, et al.. (2024). A memetic algorithm for a real-world dynamic pickup and delivery problem. Memetic Computing. 16(2). 203–217.
5.
Chen, Guangyi, Yuejie Chi, Biwei Huang, et al.. (2024). Learning Discrete Concepts in Latent Hierarchical Models. 36938–36975. 1 indexed citations
6.
Kong, Lingjing, Guangyi Chen, Eric P. Xing, et al.. (2023). Understanding Masked Autoencoders via Hierarchical Latent Variable Models. 7918–7928. 15 indexed citations
7.
8.
Kong, Lingjing, et al.. (2021). Consensus Control for Decentralized Deep Learning. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 5686–5696. 11 indexed citations
9.
Zhou, Ying, Lingjing Kong, Yiqiao Cai, et al.. (2020). A Decomposition-Based Local Search for Large-Scale Many-Objective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows. IEEE Systems Journal. 14(4). 5253–5264. 15 indexed citations
10.
Lin, Tao, Lingjing Kong, Sebastian U. Stich, & Martin Jaggi. (2020). Ensemble Distillation for Robust Model Fusion in Federated Learning. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 33. 2351–2363. 3 indexed citations
11.
Mi, Fei, Lingjing Kong, Tao Lin, Kaicheng Yu, & Boi Faltings. (2020). Generalized Class Incremental Learning. 970–974. 22 indexed citations
12.
13.
Zhou, Ying, et al.. (2019). Ensemble of multi-objective metaheuristic algorithms for multi-objective unconstrained binary quadratic programming problem. Applied Soft Computing. 81. 105485–105485. 9 indexed citations
14.
Kong, Lingjing, Cheng Chen, Yunlu Wang, & Harald Haas. (2018). Power Consumption Evaluation in High Speed Visible Light Communication Systems. 1–6. 6 indexed citations
15.
16.
Kong, Lingjing, et al.. (2018). Fast Abnormal Identification for Large Scale Internet Traffic. 117–120. 2 indexed citations
17.
Kong, Lingjing, et al.. (2017). Identification of Abnormal Network Traffic Using Support Vector Machine. 288–292. 16 indexed citations
18.
Huang, Guowei, et al.. (2017). A Bandwidth Allocation Policy for Helpers in Cloud-Assisted P2P Video-on-Demand Systems. 7–12. 4 indexed citations
19.
20.
Kong, Lingjing, et al.. (2012). Use of Distributed Trustworthy Node to Secure AS_PATH. 35–38. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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