Dustin Arendt

749 citations
35 papers · 410 indexed · h-index 13
Topics
Complex Network Analysis Techniques (13 papers)Data Visualization and Analytics (11 papers)Topic Modeling (6 papers)
Partner nations
United States

In The Last Decade

Dustin Arendt

33 papers receiving 392 citations

Peers

Dustin Arendt
Comparison fields: 5 of 71
  • Artificial Intelligence 219
  • Computer Vision and Pattern Recognition 112
  • Statistical and Nonlinear Physics 76
  • Information Systems 68
  • Sociology and Political Science 63
Replace Pradeep K. Murukannaiah with:
Pradeep K. Murukannaiah United States
Tim Donkers Germany
Avishek Anand Germany
Seema Nagar India
Dominik Kowald Austria
Roy Ka-Wei Lee Singapore
Mrinmaya Sachan Switzerland
Fatih Gedikli Germany
Viviane P. Moreira Brazil
Wanjun Zhong China
Dustin Arendt relative to Pradeep K. Murukannaiah United States Pradeep K. Murukannaiah's profile →
Citations per field
00.5×4.1×
Pradeep K. Murukannaiah · 1×
Citations per year

Countries citing papers authored by Dustin Arendt

Since Specialization
Citations

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

Fields of papers citing papers by Dustin Arendt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dustin Arendt

This figure shows the co-authorship network connecting the top 25 collaborators of Dustin Arendt. A scholar is included among the top collaborators of Dustin Arendt 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 Dustin Arendt. Dustin Arendt 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
#WorkIndexed citations
1 7
2 1
3 2
4 1
5 4
6 24
7 8
8 18
9 2
10 14
11 13
12 13
13 5
14 16
15 4
16 19
17 2
18 2
19 1
20 17

About Dustin Arendt

Dustin Arendt is a scholar working on Statistical and Nonlinear Physics, Health Informatics and Computer Vision and Pattern Recognition, having authored 35 papers that have together received 410 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (13 papers), Data Visualization and Analytics (11 papers) and Topic Modeling (6 papers). The work is most often cited by research in Health Informatics (21 citations), Artificial Intelligence (219 citations) and Safety Research (57 citations). Dustin Arendt has collaborated with scholars based in United States. Frequent co-authors include Fumeng Yang, Jean Scholtz, Svitlana Volkova, Leslie M. Blaha, Wenwen Dou, Celeste Lyn Paul, Russ Burtner, Meg Pirrung, Prasha Shrestha and Suraj Maharjan. Their work appears in journals such as PLoS ONE, IEEE Transactions on Nuclear Science and Computer Graphics Forum.

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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