Ali Pınar

1.7k total citations
42 papers, 906 citations indexed

About

Ali Pınar is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Ali Pınar has authored 42 papers receiving a total of 906 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Statistical and Nonlinear Physics, 18 papers in Computer Networks and Communications and 12 papers in Artificial Intelligence. Recurrent topics in Ali Pınar's work include Complex Network Analysis Techniques (19 papers), Parallel Computing and Optimization Techniques (9 papers) and Graph Theory and Algorithms (8 papers). Ali Pınar is often cited by papers focused on Complex Network Analysis Techniques (19 papers), Parallel Computing and Optimization Techniques (9 papers) and Graph Theory and Algorithms (8 papers). Ali Pınar collaborates with scholars based in United States, Türkiye and Mexico. Ali Pınar's co-authors include Michael T. Heath, C. Seshadhri, Tamara G. Kolda, Cevdet Aykanat, Ahmet Erdem Sarıyüce, Todd Plantenga, Neng Fan, Madhav Jha, Isabelle Stanton and Curtis L. Janssen and has published in prestigious journals such as IEEE Transactions on Power Systems, BMC Bioinformatics and Journal of the ACM.

In The Last Decade

Ali Pınar

41 papers receiving 873 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ali Pınar United States	14	453	280	279	252	161	42	906
Daniel Chavarría-Miranda United States	14	470 1.0×	350 1.3×	184 0.7×	223 0.9×	140 0.9×	51	782
John Feo United States	15	479 1.1×	404 1.4×	91 0.3×	210 0.8×	170 1.1×	58	758
Daniel M. Roy United States	13	270 0.6×	135 0.5×	100 0.4×	659 2.6×	121 0.8×	51	1.1k
Henning Meyerhenke Germany	15	269 0.6×	71 0.3×	269 1.0×	185 0.7×	160 1.0×	54	619
L. Hagen United States	10	208 0.5×	283 1.0×	232 0.8×	399 1.6×	347 2.2×	16	1.2k
Kanat Tangwongsan United States	16	462 1.0×	222 0.8×	120 0.4×	376 1.5×	199 1.2×	34	829
Jason Riedy United States	11	235 0.5×	102 0.4×	185 0.7×	184 0.7×	251 1.6×	31	604
Duane Merrill United States	13	547 1.2×	558 2.0×	74 0.3×	377 1.5×	470 2.9×	20	1.1k
Kamer Kaya Türkiye	12	260 0.6×	120 0.4×	72 0.3×	206 0.8×	108 0.7×	64	576
Irene Finocchi Italy	15	323 0.7×	127 0.5×	41 0.1×	186 0.7×	81 0.5×	63	658

Countries citing papers authored by Ali Pınar

Since Specialization

Citations

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

Fields of papers citing papers by Ali Pınar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ali Pınar

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

All Works

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20 of 20 papers shown

Pınar, Ali, et al.. (2024). Neuromorphic Monte Carlo Tree Search Methods for Shortest Path Interdiction. 307–311.

Vugrin, Eric D., et al.. (2023). Experimental Validation of a Command and Control Traffic Detection Model. IEEE Transactions on Dependable and Secure Computing. 21(3). 1084–1097. 1 indexed citations

Schulz, Timothy J., et al.. (2022). Defender Policy Evaluation and Resource Allocation With MITRE ATT&CK Evaluations Data. IEEE Transactions on Dependable and Secure Computing. 20(3). 1909–1926. 9 indexed citations

Pınar, Ali. (2021). Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 9 indexed citations

Pınar, Ali, et al.. (2021). ElGA. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–15. 2 indexed citations

Pınar, Ali, et al.. (2021). Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations

Whitmore, Leanne S., et al.. (2019). RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms. BMC Bioinformatics. 20(1). 461–461. 10 indexed citations

Sarıyüce, Ahmet Erdem, C. Seshadhri, & Ali Pınar. (2017). Parallel Local Algorithms for Core, Truss, and Nucleus Decompositions.. arXiv (Cornell University). 8 indexed citations

Jha, Madhav, Ali Pınar, & C. Seshadhri. (2015). Counting triangles in real-world graph streams: Dealing with repeated edges and time windows. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1507–1514. 7 indexed citations

10.

Kolda, Tamara G., Ali Pınar, Todd Plantenga, & C. Seshadhri. (2014). A Scalable Generative Graph Model with Community Structure. SIAM Journal on Scientific Computing. 36(5). C424–C452. 87 indexed citations

11.

Subbian, Karthik, Ayush Singhal, Tamara G. Kolda, Ali Pınar, & Jaideep Srivastava. (2013). On Reciprocity in Massively Multi-player Online Game Networks. arXiv (Cornell University). 2 indexed citations

12.

Seshadhri, C., et al.. (2013). The importance of directed triangles with reciprocity: patterns and algorithms.. arXiv (Cornell University). 4 indexed citations

13.

Seshadhri, C., Ali Pınar, & Tamara G. Kolda. (2013). An in-depth analysis of stochastic Kronecker graphs. Journal of the ACM. 60(2). 1–32. 26 indexed citations

14.

Jha, Madhav, C. Seshadhri, & Ali Pınar. (2013). When a Graph is not so Simple: Counting Triangles in Multigraph Streams.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3 indexed citations

15.

Quinn, Christopher J., Ali Pınar, & Negar Kiyavash. (2013). Optimal bounded-degree approximations of joint distributions of networks of stochastic processes. 2264–2268. 2 indexed citations

16.

Seshadhri, C., Ali Pınar, & Tamara G. Kolda. (2012). Fast triangle counting through wedge sampling.. arXiv (Cornell University). 13 indexed citations

17.

Stanton, Isabelle & Ali Pınar. (2012). Constructing and sampling graphs with a prescribed joint degree distribution. ACM Journal of Experimental Algorithmics. 17. 30 indexed citations

18.

Seshadhri, C., Ali Pınar, & Tamara G. Kolda. (2011). A Hitchhiker's Guide to Choosing Parameters of Stochastic Kronecker Graphs. arXiv (Cornell University). 2 indexed citations

19.

Janssen, Curtis L., et al.. (2010). A Simulator for Large-Scale Parallel Computer Architectures. International Journal of Distributed Systems and Technologies. 1(2). 57–73. 84 indexed citations

20.

Pınar, Ali, et al.. (2008). One-dimensional partitioning for heterogeneous systems: Theory and practice. Journal of Parallel and Distributed Computing. 68(11). 1473–1486. 12 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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