Di Jin

6.6k total citations · 3 hit papers
228 papers, 4.0k citations indexed

About

Di Jin is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Di Jin has authored 228 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 140 papers in Artificial Intelligence, 99 papers in Statistical and Nonlinear Physics and 27 papers in Computer Vision and Pattern Recognition. Recurrent topics in Di Jin's work include Complex Network Analysis Techniques (97 papers), Advanced Graph Neural Networks (86 papers) and Opinion Dynamics and Social Influence (42 papers). Di Jin is often cited by papers focused on Complex Network Analysis Techniques (97 papers), Advanced Graph Neural Networks (86 papers) and Opinion Dynamics and Social Influence (42 papers). Di Jin collaborates with scholars based in China, United States and Australia. Di Jin's co-authors include Dongxiao He, Weixiong Zhang, Xiaochun Cao, Liang Yang, Pengfei Jiao, Dayou Liu, Zhizhi Yu, Liang Yang, Jianwu Dang and Eli Lifland and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Advanced Functional Materials.

In The Last Decade

Di Jin

209 papers receiving 3.9k citations

Hit Papers

TextAttack: A Framework for Adversarial Attacks, Data Aug... 2020 2026 2022 2024 2020 2021 2024 100 200 300

Peers

Di Jin
Comparison fields: 5 of 164
  • Artificial Intelligence 2.3k
  • Statistical and Nonlinear Physics 1.8k
  • Information Systems 595
  • Computer Vision and Pattern Recognition 537
  • Computer Networks and Communications 446
Replace Ganqu Cui with:
Ganqu Cui China
Zhengyan Zhang China
Zhichao Han China
Yu Rong China
Yue Xu Australia
Stephan Günnemann Germany
Tingyang Xu China
Ziwei Zhang China
Lawrence B. Holder United States
Alessandro Sperduti Italy
Ganqu Cui China View profile →
Citations per field, relative to Di Jin
Di Jin · 1×
Citations per year, relative to Di Jin
Di Jin · 1×

Countries citing papers authored by Di Jin

Since Specialization
Citations

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

Fields of papers citing papers by Di Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Di Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Di Jin. A scholar is included among the top collaborators of Di 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 Di Jin. Di 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 0
3 2
4 1
5 0
6
Survey of spectral clustering based on graph theory breakdown →
49
7 32
8 1
9 0
10 1
11 3
12 7
13 2
14 5
15 5
16 19
17 5
18 15
19 55
20
A Markov random walk under constraint for discovering overlapping communities in complex networks
60

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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