Beom Jun Kim

7.3k total citations · 2 hit papers
160 papers, 5.2k citations indexed

About

Beom Jun Kim is a scholar working on Statistical and Nonlinear Physics, Condensed Matter Physics and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Beom Jun Kim has authored 160 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Statistical and Nonlinear Physics, 60 papers in Condensed Matter Physics and 33 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Beom Jun Kim's work include Theoretical and Computational Physics (47 papers), Opinion Dynamics and Social Influence (41 papers) and Complex Network Analysis Techniques (35 papers). Beom Jun Kim is often cited by papers focused on Theoretical and Computational Physics (47 papers), Opinion Dynamics and Social Influence (41 papers) and Complex Network Analysis Techniques (35 papers). Beom Jun Kim collaborates with scholars based in South Korea, Sweden and United States. Beom Jun Kim's co-authors include Petter Holme, Seung Kee Han, Chang No Yoon, M. Y. Choi, Hyunsuk Hong, Petter Minnhagen, Seung Ki Baek, Hawoong Jeong, Hyunggyu Park and Ala Trusina and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Advanced Materials.

In The Last Decade

Beom Jun Kim

149 papers receiving 5.0k citations

Hit Papers

Attack vulnerability of complex networks 2002 2026 2010 2018 2002 2002 400 800 1.2k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Beom Jun Kim South Korea 29 3.0k 1.4k 752 684 440 160 5.2k
A. V. Goltsev Russia 26 3.4k 1.1× 781 0.6× 1.5k 2.0× 331 0.5× 468 1.1× 96 6.0k
Chaoming Song United States 31 3.2k 1.0× 1.1k 0.8× 567 0.8× 718 1.0× 802 1.8× 74 9.2k
B. Kahng South Korea 43 3.6k 1.2× 992 0.7× 1.3k 1.7× 288 0.4× 901 2.0× 206 7.1k
Marián Boguñá Spain 39 6.0k 2.0× 1.2k 0.8× 505 0.7× 889 1.3× 944 2.1× 93 8.5k
Ginestra Bianconi United Kingdom 48 5.6k 1.8× 1.4k 1.0× 918 1.2× 901 1.3× 1.4k 3.2× 192 9.4k
James P. Gleeson Ireland 31 3.1k 1.0× 806 0.6× 226 0.3× 768 1.1× 508 1.2× 116 5.5k
Reuven Cohen Israel 34 5.6k 1.8× 2.5k 1.8× 632 0.8× 651 1.0× 753 1.7× 145 8.4k
Miguel A. Muñoz Spain 39 2.3k 0.8× 799 0.6× 1.7k 2.3× 435 0.6× 552 1.3× 160 5.5k
Zoltán Toroczkai United States 35 1.9k 0.6× 733 0.5× 535 0.7× 286 0.4× 460 1.0× 96 5.7k
Lazaros K. Gallos United States 21 3.0k 1.0× 608 0.4× 233 0.3× 416 0.6× 573 1.3× 57 4.3k

Countries citing papers authored by Beom Jun Kim

Since Specialization
Citations

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

Fields of papers citing papers by Beom Jun Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Beom Jun Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Beom Jun Kim. A scholar is included among the top collaborators of Beom Jun Kim 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 Beom Jun Kim. Beom Jun Kim 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.
Kim, Beom Jun, et al.. (2025). Preventing microcracks between directed energy deposited Hastelloy X and IN792 substrate by adding IN625 buffer layer. Additive manufacturing. 110. 104947–104947.
2.
Seo, Jin H. & Beom Jun Kim. (2025). Opinion dynamics model of collaborative learning. Physica A Statistical Mechanics and its Applications. 672. 130670–130670.
3.
Park, Hye Jin, Christian Hilbe, Martin A. Nowak, Beom Jun Kim, & Hyeong-Chai Jeong. (2023). Vacancies in growing habitats promote the evolution of cooperation. Journal of Theoretical Biology. 575. 111629–111629.
4.
Kim, Beom Jun, et al.. (2023). A multiresolution framework for the analysis of community structure in international trade networks. Scientific Reports. 13(1). 5721–5721. 6 indexed citations
5.
Kim, Beom Jun, et al.. (2021). Power-grid stability prediction using transferable machine learnings.. arXiv (Cornell University).
6.
Kim, Beom Jun, et al.. (2021). Power-grid stability predictions using transferable machine learning. Chaos An Interdisciplinary Journal of Nonlinear Science. 31(12). 123127–123127. 13 indexed citations
7.
Kim, Beom Jun, et al.. (2020). Discontinuous phase transition in the Kuramoto model with asymmetric dynamic interaction. Physical review. E. 102(5). 52207–52207. 4 indexed citations
8.
Kim, Beom Jun, et al.. (2019). Confusion scheme in machine learning detects double phase transitions and quasi-long-range order. Physical review. E. 99(4). 43308–43308. 18 indexed citations
9.
Baek, Seung Ki, et al.. (2013). Universal statistics of the knockout tournament. Scientific Reports. 3(1). 3198–3198. 2 indexed citations
10.
Yi, Sudo, Seung Ki Baek, Chen-Ping Zhu, & Beom Jun Kim. (2013). Phase transition in a coevolving network of conformist and contrarian voters. Physical Review E. 87(1). 12806–12806. 21 indexed citations
11.
Baek, Seung Ki, Jung-Kyoo Choi, & Beom Jun Kim. (2012). Dworkin’s Paradox. PLoS ONE. 7(6). e38529–e38529. 1 indexed citations
12.
Park, Su‐Chan, et al.. (2009). Reentrant phase transition in a predator-prey model. Physical Review E. 79(6). 66114–66114. 3 indexed citations
13.
Kim, DH, Beom Jun Kim, & Hawoong Jeong. (2005). Universality Class of the Fiber Bundle Model on Complex Networks. Physical Review Letters. 94(2). 25501–25501. 52 indexed citations
14.
Park, Sung Min, Petter Holme, & Beom Jun Kim. (2004). Student network in Ajou University based on the course registration data. New Physics Sae Mulli. 49(5). 399–405. 3 indexed citations
15.
Kim, Beom Jun. (2004). Performance of networks of artificial neurons: The role of clustering. Physical Review E. 69(4). 45101–45101. 91 indexed citations
16.
Holme, Petter, Fredrik Liljeros, Christofer Edling, & Beom Jun Kim. (2003). Network bipartivity. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 68(5). 56107–56107. 94 indexed citations
17.
Hong, Hyunsuk, Beom Jun Kim, & M. Y. Choi. (2003). Optimal size of a complex network. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 67(4). 46101–46101. 12 indexed citations
18.
Holme, Petter & Beom Jun Kim. (2002). Growing scale-free networks with tunable clustering. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 65(2). 26107–26107. 626 indexed citations breakdown →
19.
Hong, Hyunsuk, M. Y. Choi, & Beom Jun Kim. (2002). Synchronization on small-world networks. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 65(2). 26139–26139. 335 indexed citations
20.
Minnhagen, Petter, Beom Jun Kim, & Hans Weber. (2001). Evidence of Two Distinct Dynamic Critical Exponents in Connection with Vortex Physics. Physical Review Letters. 87(3). 37002–37002. 9 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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