Karl Jiang
Impact in
-
- Complex Network Analysis Techniques
-
- Graph Theory and Algorithms
Papers in
-
- Advanced Graph Neural Networks 4
- Algorithms and Data Compression 2
-
- Complex Network Analysis Techniques 6
- Co-authors
- David A. Bader (6 shared papers)David Ediger (5 shared papers)Jason Riedy (4 shared papers)Maya Çakmak (1 shared paper)Andrea L. Thomaz (2 shared papers)Barış Akgün (1 shared paper)Courtney D. Corley (1 shared paper)Kamesh Madduri (1 shared paper)
- Journals
- IEEE Transactions on Parallel and Distributed Systems (2 papers)International Journal of Social Robotics (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)SMARTech Repository (Georgia Institute of Technology) (1 paper)
- Partner nations
- United States
In The Last Decade
Karl Jiang
10 papers receiving 455 citations
Peers
Comparison fields: 5 of 68
- Statistical and Nonlinear Physics 162
- Computer Vision and Pattern Recognition 158
- Artificial Intelligence 222
- Hardware and Architecture 46
- Computer Networks and Communications 127
Countries citing papers authored by Karl Jiang
This map shows the geographic impact of Karl Jiang'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 Karl Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karl Jiang more than expected).
Fields of papers citing papers by Karl Jiang
This network shows the impact of papers produced by Karl Jiang. 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 Karl Jiang. The network helps show where Karl Jiang may publish in the future.
Co-authors
The 14 scholars most cited alongside Karl Jiang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 135 | |
| 2 | 2010 | 104 | |
| 3 | 2009 | 103 | |
| 4 | 2010 | 55 | |
| 5 | 2007 | 28 | |
| 6 | 2012 | 18 | |
| 7 | 2009 | 14 | |
| 8 | 2007 | 14 | |
| 9 | Detecting Communities from Given Seeds in Social Networks | 2011 | 13 |
| 10 | 2010 | 2 |
About Karl Jiang
Karl Jiang is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Molecular Biology, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 10 papers that have together received 486 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (6 papers), Advanced Graph Neural Networks (4 papers), Genomics and Phylogenetic Studies (2 papers), Graph Theory and Algorithms (2 papers), Bioinformatics and Genomic Networks (2 papers), Gene expression and cancer classification (2 papers), Algorithms and Data Compression (2 papers) and Caching and Content Delivery (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (162 citations), Computer Vision and Pattern Recognition (158 citations), Artificial Intelligence (222 citations), Hardware and Architecture (46 citations) and Computer Networks and Communications (127 citations). Karl Jiang has collaborated with scholars based in United States. Frequent co-authors include David A. Bader, David Ediger, Jason Riedy, Maya Çakmak, Andrea L. Thomaz, Barış Akgün, Courtney D. Corley, Kamesh Madduri, Daniel Chavarría-Miranda and Carlos P. Sosa. Their work appears in journals such as IEEE Transactions on Parallel and Distributed Systems, International Journal of Social Robotics, Zenodo (CERN European Organization for Nuclear Research) and SMARTech Repository (Georgia Institute of Technology).
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.