Karl Jiang

813 total citations
10 papers, 486 citations indexed

About

Karl Jiang is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Molecular Biology. According to data from OpenAlex, Karl Jiang has authored 10 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Statistical and Nonlinear Physics and 4 papers in Molecular Biology. Recurrent topics in Karl Jiang's work include Complex Network Analysis Techniques (6 papers), Advanced Graph Neural Networks (4 papers) and Graph Theory and Algorithms (2 papers). Karl Jiang is often cited by papers focused on Complex Network Analysis Techniques (6 papers), Advanced Graph Neural Networks (4 papers) and Graph Theory and Algorithms (2 papers). Karl Jiang collaborates with scholars based in United States. Karl Jiang's 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 and has published in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems, International Journal of Social Robotics and SMARTech Repository (Georgia Institute of Technology).

In The Last Decade

Karl Jiang

10 papers receiving 455 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Karl Jiang United States	9	222	162	158	127	108	10	486
Zhen Peng China	11	530 2.4×	179 1.1×	99 0.6×	246 1.9×	16 0.1×	30	730
Xiaoyu Ding China	11	200 0.9×	133 0.8×	153 1.0×	104 0.8×	67 0.6×	30	512
Joseph J. Pfeiffer United States	8	117 0.5×	81 0.5×	41 0.3×	31 0.2×	31 0.3×	28	240
Glenn Fink United States	12	157 0.7×	138 0.9×	224 1.4×	248 2.0×	27 0.3×	33	537
Xiaofei Zhu China	14	249 1.1×	143 0.9×	115 0.7×	68 0.5×	22 0.2×	62	647
Matej Balog United Kingdom	4	218 1.0×	26 0.2×	44 0.3×	45 0.4×	21 0.2×	5	470
Qiong Luo Hong Kong	9	160 0.7×	30 0.2×	151 1.0×	104 0.8×	13 0.1×	30	343
Jianzong Wang China	15	702 3.2×	94 0.6×	143 0.9×	128 1.0×	19 0.2×	156	1000
Shanqing Yu China	12	252 1.1×	129 0.8×	36 0.2×	73 0.6×	39 0.4×	57	434

Countries citing papers authored by Karl Jiang

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Karl Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Karl Jiang. A scholar is included among the top collaborators of Karl Jiang 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 Karl Jiang. Karl Jiang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Akgün, Barış, Maya Çakmak, Karl Jiang, & Andrea L. Thomaz. (2012). Keyframe-based Learning from Demonstration. International Journal of Social Robotics. 4(4). 343–355. 135 indexed citations

2.

Ediger, David, Karl Jiang, Jason Riedy, & David A. Bader. (2012). GraphCT: Multithreaded Algorithms for Massive Graph Analysis. IEEE Transactions on Parallel and Distributed Systems. 24(11). 2220–2229. 18 indexed citations

3.

Riedy, Jason, et al.. (2011). Detecting Communities from Given Seeds in Social Networks. SMARTech Repository (Georgia Institute of Technology). 13 indexed citations

4.

Ediger, David, Karl Jiang, Jason Riedy, David A. Bader, & Courtney D. Corley. (2010). Massive Social Network Analysis: Mining Twitter for Social Good. 583–593. 104 indexed citations

5.

Ediger, David, Karl Jiang, Jason Riedy, & David A. Bader. (2010). Massive streaming data analytics: A case study with clustering coefficients. 1–8. 55 indexed citations

6.

Wiltgen, Bryan, et al.. (2010). The interplay of context and emotion for non-anthropomorphic robots. Zenodo (CERN European Organization for Nuclear Research). 658–663. 2 indexed citations

7.

Jiang, Karl, David Ediger, & David A. Bader. (2009). Generalizing k-Betweenness Centrality Using Short Paths and a Parallel Multithreaded Implementation. 542–549. 14 indexed citations

8.

Madduri, Kamesh, David Ediger, Karl Jiang, David A. Bader, & Daniel Chavarría-Miranda. (2009). A faster parallel algorithm and efficient multithreaded implementations for evaluating betweenness centrality on massive datasets. 1–8. 103 indexed citations

9.

Jiang, Karl, et al.. (2007). An Efficient Parallel Implementation of the Hidden Markov Methods for Genomic Sequence-Search on a Massively Parallel System. IEEE Transactions on Parallel and Distributed Systems. 19(1). 15–23. 14 indexed citations

10.

Sosa, Carlos P., et al.. (2007). Parallel genomic sequence-search on a massively parallel system. 59–68. 28 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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