Jun Wu

2.9k citations
156 papers · 2.0k · h-index 22

Impact in

Papers in

Jun Wu

139 papers receiving 1.9k citations

Peers

Jun Wu
Comparison fields: 5 of 141
  • Statistical and Nonlinear Physics 853
  • Geometry and Topology 200
  • Computer Networks and Communications 488
  • Condensed Matter Physics 144
  • Civil and Structural Engineering 205
Replace Gang Yan with:
Gang Yan China
Raissa M. D’Souza United States
Dieter Armbruster United States
Lidia A. Braunstein Argentina
Gerald Paul United States
Keren Erez Israel
Shiming Chen China
Peter Grindrod United Kingdom
Mao-Bin Hu China
Lili Rong China
Jun Wu relative to Gang Yan China Gang Yan's profile →
Citations per field
00.5×1.5×2.1×
Gang Yan · 1×
Citations per year

Countries citing papers authored by Jun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Jun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Wu Line = papers co-authored together Jun Wu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 156 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011199
2 2010190
3 1999165
4 2015159
5 200781
6
Network Structure Entropy and Its Application to Scale-free Networks
200461
7 201561
8 201642
9 201642
10 201840
11 201234
12 201632
13 201730
14 200728
15 201927
16 201924
17 201824
18 201823
19 201522
20 201022

About Jun Wu

Jun Wu is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications, Electrical and Electronic Engineering, Management Science and Operations Research and Control and Systems Engineering, having authored 156 papers that have together received 2.0k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (60 papers), Opinion Dynamics and Social Influence (35 papers), Graph theory and applications (15 papers), Network Security and Intrusion Detection (13 papers), Auction Theory and Applications (11 papers), Opportunistic and Delay-Tolerant Networks (9 papers), Game Theory and Voting Systems (8 papers) and Optimization and Search Problems (7 papers). The work is most often cited by research in Statistical and Nonlinear Physics (853 citations), Geometry and Topology (200 citations), Computer Networks and Communications (488 citations), Condensed Matter Physics (144 citations) and Civil and Structural Engineering (205 citations). Jun Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Hongzhong Deng, Yuejin Tan, Mauricio Barahona, Guansheng Peng, Ye Deng, Suoyi Tan, Yapeng Li, Xin Lü, M. Hamilton and J. Diaz. Their work appears in journals such as Chaos An Interdisciplinary Journal of Nonlinear Science, Chinese Physics Letters, Physica A Statistical Mechanics and its Applications, Information Processing & Management and Reliability Engineering & System Safety.

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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