Guojing Cong

1.3k total citations
63 papers, 557 citations indexed

About

Guojing Cong is a scholar working on Computer Networks and Communications, Artificial Intelligence and Hardware and Architecture. According to data from OpenAlex, Guojing Cong has authored 63 papers receiving a total of 557 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Networks and Communications, 20 papers in Artificial Intelligence and 17 papers in Hardware and Architecture. Recurrent topics in Guojing Cong's work include Parallel Computing and Optimization Techniques (16 papers), Interconnection Networks and Systems (11 papers) and Graph Theory and Algorithms (10 papers). Guojing Cong is often cited by papers focused on Parallel Computing and Optimization Techniques (16 papers), Interconnection Networks and Systems (11 papers) and Graph Theory and Algorithms (10 papers). Guojing Cong collaborates with scholars based in United States, Japan and China. Guojing Cong's co-authors include David A. Bader, Vijay Saraswat, John Feo, Konstantin Makarychev, Doug Lea, I‐Hsin Chung, Sriram Krishnamoorthy, Tong Wen, George Almási and Onkar Bhardwaj and has published in prestigious journals such as Journal of Computational Physics, BMC Bioinformatics and Journal of Chemical Theory and Computation.

In The Last Decade

Guojing Cong

56 papers receiving 534 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guojing Cong United States 12 303 214 214 146 102 63 557
Kuen Hung Tsoi United Kingdom 14 260 0.9× 187 0.9× 333 1.6× 188 1.3× 91 0.9× 35 733
Kishore Kothapalli India 14 416 1.4× 197 0.9× 217 1.0× 191 1.3× 64 0.6× 76 665
Tze Meng Low United States 11 186 0.6× 101 0.5× 276 1.3× 145 1.0× 23 0.2× 38 464
Scott Beamer United States 11 338 1.1× 372 1.7× 290 1.4× 259 1.8× 158 1.5× 23 960
Tarek S. Abdelrahman Canada 15 628 2.1× 128 0.6× 685 3.2× 140 1.0× 126 1.2× 72 928
E. Wes Bethel United States 15 177 0.6× 208 1.0× 76 0.4× 80 0.5× 42 0.4× 38 511
Zhihao Jia United States 15 195 0.6× 299 1.4× 178 0.8× 408 2.8× 125 1.2× 34 738
Benedict R. Gaster United Kingdom 7 306 1.0× 83 0.4× 334 1.6× 124 0.8× 85 0.8× 31 546
Jesper Larsson Träff Austria 18 837 2.8× 81 0.4× 716 3.3× 130 0.9× 151 1.5× 113 1.1k
Cong Fu China 13 138 0.5× 278 1.3× 104 0.5× 291 2.0× 122 1.2× 31 618

Countries citing papers authored by Guojing Cong

Since Specialization
Citations

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

Fields of papers citing papers by Guojing Cong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guojing Cong

This figure shows the co-authorship network connecting the top 25 collaborators of Guojing Cong. A scholar is included among the top collaborators of Guojing Cong 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 Guojing Cong. Guojing Cong 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.
Feng, Rui, et al.. (2024). Derivative-based pre-training of graph neural networks for materials property predictions. Digital Discovery. 3(3). 586–593. 3 indexed citations
2.
Cong, Guojing & Scott S. Auerbach. (2023). Clustering High-dimensional Toxicogenomics Data with Rare Signals. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 30. 608–614. 1 indexed citations
3.
Zhang, Peng, Guojing Cong, James Kozloski, et al.. (2023). AI-aided multiscale modeling of physiologically-significant blood clots. Computer Physics Communications. 287. 108718–108718.
4.
Zhang, Peng, et al.. (2022). Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells. Frontiers in Molecular Biosciences. 8. 812248–812248. 6 indexed citations
5.
Cong, Guojing & Seung–Hwan Lim. (2021). Versatile feature learning with graph convolutions and graph structures. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 669–677. 3 indexed citations
6.
Lim, Seung–Hwan, Junghoon Chae, Guojing Cong, et al.. (2021). Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis. 2021 IEEE International Conference on Big Data (Big Data). 4381–4386. 2 indexed citations
7.
Cong, Guojing, et al.. (2020). Fast Training of Deep Neural Networks for Speech Recognition. 6884–6888. 4 indexed citations
8.
Yang, Chih-Chieh, Giacomo Domeniconi, Leili Zhang, & Guojing Cong. (2020). Design of AI-Enhanced Drug Lead Optimization Workflow for HPC and Cloud. 5861–5863. 5 indexed citations
9.
Zhang, Peng, et al.. (2020). Artificial intelligence for accelerating time integrations in multiscale modeling. Journal of Computational Physics. 427. 110053–110053. 19 indexed citations
10.
Cong, Guojing, et al.. (2017). Accelerating deep neural network learning for speech recognition on a cluster of GPUs. 1–8. 3 indexed citations
11.
Bhardwaj, Onkar & Guojing Cong. (2016). Practical efficiency of asynchronous stochastic gradient descent. IEEE International Conference on High Performance Computing, Data, and Analytics. 56–62. 2 indexed citations
12.
Chung, I‐Hsin, et al.. (2012). Application data prefetching on the IBM blue gene/Q supercomputer. IEEE International Conference on High Performance Computing, Data, and Analytics. 1–8. 9 indexed citations
13.
Cong, Guojing, et al.. (2011). A Systematic Approach toward Automated Performance Analysis and Tuning. IEEE Transactions on Parallel and Distributed Systems. 23(3). 426–435. 7 indexed citations
14.
Cong, Guojing & Konstantin Makarychev. (2011). Optimizing Large-Scale Graph Analysis on a Multi-threaded, Multi-core Platform. 1619. 688–697. 10 indexed citations
15.
Cong, Guojing & Konstantin Makarychev. (2009). Improving Memory Access Locality for Large-Scale Graph Analysis Applications.. 121–127. 1 indexed citations
16.
Cong, Guojing, et al.. (2008). Solving Large, Irregular Graph Problems Using Adaptive Work-Stealing. 536–545. 64 indexed citations
17.
Chung, I‐Hsin, et al.. (2008). A framework for automated performance bottleneck detection. Proceedings - IEEE International Parallel and Distributed Processing Symposium. 1–7. 11 indexed citations
18.
Cong, Guojing & Tong Wen. (2007). Locality behavior of parallel and sequential algorithms for irregular graph problems. European Journal Of Haematology. 105(3). 391–397. 1 indexed citations
19.
Cong, Guojing & David A. Bader. (2006). An Empirical Analysis of Parallel Random Permutation Algorithms on SMPs. SMARTech Repository (Georgia Institute of Technology). 27–34. 4 indexed citations
20.
Cong, Guojing & David A. Bader. (2004). Lock-free parallel algorithms: An experimental study. 2 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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2026