Hai Jin

32.9k total citations · 2 hit papers
1.5k papers, 20.4k citations indexed

About

Hai Jin is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Hai Jin has authored 1.5k papers receiving a total of 20.4k indexed citations (citations by other indexed papers that have themselves been cited), including 846 papers in Computer Networks and Communications, 602 papers in Information Systems and 439 papers in Artificial Intelligence. Recurrent topics in Hai Jin's work include Cloud Computing and Resource Management (343 papers), Caching and Content Delivery (257 papers) and Parallel Computing and Optimization Techniques (238 papers). Hai Jin is often cited by papers focused on Cloud Computing and Resource Management (343 papers), Caching and Content Delivery (257 papers) and Parallel Computing and Optimization Techniques (238 papers). Hai Jin collaborates with scholars based in China, United States and Australia. Hai Jin's co-authors include Xiaofei Liao, Fangming Liu, Deqing Zou, Qiang He, Song Wu, Yun Yang, Feifei Chen, Xuanhua Shi, Haikun Liu and Hanhua Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Hai Jin

1.4k papers receiving 19.6k citations

Hit Papers

Computation Offloading Toward Edge Computing 2019 2026 2021 2023 2019 2019 100 200 300

Peers

Hai Jin
Comparison fields: 5 of 178
  • Computer Networks and Communications 11.9k
  • Information Systems 9.5k
  • Artificial Intelligence 5.5k
  • Computer Vision and Pattern Recognition 3.7k
  • Electrical and Electronic Engineering 2.3k
Replace Matei Zaharia with:
Matei Zaharia United States
Armando Fox United States
Anthony D. Joseph United States
Yang Xiang Australia
Joseph M. Hellerstein United States
Albert Y. Zomaya Australia
Sanjay Ghemawat United States
Xiaojiang Du United States
Dawn Song United States
Laurence T. Yang Canada
Matei Zaharia United States View profile →
Citations per field, relative to Hai Jin
Hai Jin · 1×
Citations per year, relative to Hai Jin
Hai Jin · 1×

Countries citing papers authored by Hai Jin

Since Specialization
Citations

This map shows the geographic impact of Hai Jin'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 Hai Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hai Jin more than expected).

Fields of papers citing papers by Hai Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hai Jin. 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 Hai Jin. The network helps show where Hai Jin may publish in the future.

Co-authorship network of co-authors of Hai Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Hai Jin. A scholar is included among the top collaborators of Hai Jin 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 Hai Jin. Hai Jin 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
# Work Indexed citations
1 0
2 1
3
StreamBox: A Lightweight GPU SandBox for Serverless Inference Workflow
0
4 4
5 1
6 0
7 3
8 10
9 1
10 3
11 3
12 3
13 0
14 10
15 10
16 21
17 54
18 11
19 84
20 17

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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