Nina Narodytska

2.6k total citations
53 papers, 819 citations indexed

About

Nina Narodytska is a scholar working on Artificial Intelligence, Computer Networks and Communications and Management Science and Operations Research. According to data from OpenAlex, Nina Narodytska has authored 53 papers receiving a total of 819 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 17 papers in Computer Networks and Communications and 14 papers in Management Science and Operations Research. Recurrent topics in Nina Narodytska's work include Game Theory and Voting Systems (14 papers), Auction Theory and Applications (13 papers) and Constraint Satisfaction and Optimization (12 papers). Nina Narodytska is often cited by papers focused on Game Theory and Voting Systems (14 papers), Auction Theory and Applications (13 papers) and Constraint Satisfaction and Optimization (12 papers). Nina Narodytska collaborates with scholars based in Australia, United States and Canada. Nina Narodytska's co-authors include Shiva Prasad Kasiviswanathan, Toby Walsh, João Marques‐Silva, Alexey Ignatiev, Fahiem Bacchus, Weili Nie, Ankit Patel, Jessica Davies, Lirong Xia and George Katsirelos and has published in prestigious journals such as SHILAP Revista de lepidopterología, Artificial Intelligence and Annals of Operations Research.

In The Last Decade

Nina Narodytska

49 papers receiving 791 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nina Narodytska Australia 14 575 158 147 140 129 53 819
Luigi Palopoli Italy 18 598 1.0× 460 2.9× 129 0.9× 70 0.5× 158 1.2× 105 1.0k
Tyler Lu Canada 14 418 0.7× 111 0.7× 122 0.8× 330 2.4× 366 2.8× 27 856
Erik Vee United States 19 404 0.7× 539 3.4× 126 0.9× 52 0.4× 178 1.4× 35 954
Matti J„ärvisalo Finland 17 676 1.2× 197 1.2× 257 1.7× 11 0.1× 70 0.5× 99 874
Marco Schaerf Italy 18 824 1.4× 200 1.3× 305 2.1× 28 0.2× 49 0.4× 67 1.1k
Dana Drachsler-Cohen Switzerland 8 649 1.1× 133 0.8× 43 0.3× 41 0.3× 46 0.4× 12 1.1k
Giorgio Gambosi Italy 13 226 0.4× 362 2.3× 322 2.2× 22 0.2× 54 0.4× 58 878
Yun Shen China 15 382 0.7× 205 1.3× 36 0.2× 19 0.1× 68 0.5× 75 764
Petar Tsankov Switzerland 14 814 1.4× 284 1.8× 59 0.4× 53 0.4× 52 0.4× 26 1.6k
Brian Milch United States 12 687 1.2× 137 0.9× 31 0.2× 40 0.3× 280 2.2× 19 924

Countries citing papers authored by Nina Narodytska

Since Specialization
Citations

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

Fields of papers citing papers by Nina Narodytska

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nina Narodytska

This figure shows the co-authorship network connecting the top 25 collaborators of Nina Narodytska. A scholar is included among the top collaborators of Nina Narodytska 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 Nina Narodytska. Nina Narodytska 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.
Ignatiev, Alexey, et al.. (2023). On computing probabilistic abductive explanations. International Journal of Approximate Reasoning. 159. 108939–108939. 5 indexed citations
2.
Ignatiev, Alexey, et al.. (2023). Eliminating the Impossible, Whatever Remains Must Be True: On Extracting and Applying Background Knowledge in the Context of Formal Explanations. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 4123–4131. 3 indexed citations
3.
Wu, Haoze, Clark Barrett, Mahmood Sharif, Nina Narodytska, & Gagandeep Singh. (2022). Scalable verification of GNN-based job schedulers. Proceedings of the ACM on Programming Languages. 6(OOPSLA2). 1036–1065. 4 indexed citations
4.
Davies, Jessica, Nina Narodytska, & Toby Walsh. (2021). Eliminating the Weakest Link: Making Manipulation Intractable?. Proceedings of the AAAI Conference on Artificial Intelligence. 26(1). 1333–1339.
5.
Ignatiev, Alexey, João Marques‐Silva, Nina Narodytska, & Peter J. Stuckey. (2021). Reasoning-Based Learning of Interpretable ML Models. Monash University Research Portal (Monash University). 4458–4465. 8 indexed citations
6.
Narodytska, Nina, Hongce Zhang, Aarti Gupta, & Toby Walsh. (2020). In Search for a SAT-friendly Binarized Neural Network Architecture. International Conference on Learning Representations. 4 indexed citations
7.
Suresh, Lalith, et al.. (2020). Building Scalable and Flexible Cluster Managers Using Declarative Programming. 827–844. 4 indexed citations
8.
Ignatiev, Alexey, Nina Narodytska, & João Marques‐Silva. (2019). On relating explanations and adversarial examples. Monash University Research Portal (Monash University). 32. 15857–15867. 34 indexed citations
9.
Nie, Weili, Nina Narodytska, & Ankit Patel. (2018). RelGAN: Relational Generative Adversarial Networks for Text Generation.. International Conference on Learning Representations. 78 indexed citations
10.
Narodytska, Nina & Shiva Prasad Kasiviswanathan. (2017). Simple Black-Box Adversarial Attacks on Deep Neural Networks. 1310–1318. 188 indexed citations
11.
Loreggia, Andrea, Nina Narodytska, Francesca Rossi, Kristen Brent Venable, & Toby Walsh. (2015). Controlling Elections by Replacing Candidates or Votes. Adaptive Agents and Multi-Agents Systems. 1737–1738. 6 indexed citations
12.
Bjørner, Nikolaj & Nina Narodytska. (2015). Maximum satisfiability using cores and correction sets. International Conference on Artificial Intelligence. 246–252. 7 indexed citations
13.
Aziz, Haris, Serge Gaspers, Simon Mackenzie, et al.. (2015). Manipulating the Probabilistic Serial Rule. arXiv (Cornell University). 1451–1459. 9 indexed citations
14.
Narodytska, Nina & Fahiem Bacchus. (2014). Maximum Satisfiability Using Core-Guided MaxSAT Resolution. Proceedings of the AAAI Conference on Artificial Intelligence. 28(1). 57 indexed citations
15.
Narodytska, Nina & Toby Walsh. (2013). Manipulating two stage voting rules. Adaptive Agents and Multi-Agents Systems. 423–430. 3 indexed citations
16.
Chu, Geoffrey, Serge Gaspers, Nina Narodytska, Andreas Schutt, & Toby Walsh. (2013). On the complexity of global scheduling constraints under structural restrictions. International Joint Conference on Artificial Intelligence. 503–509. 4 indexed citations
17.
Gaspers, Serge, Thomas Kalinowski, Nina Narodytska, & Toby Walsh. (2013). Coalitional manipulation for Schulze's rule. arXiv (Cornell University). 431–438. 3 indexed citations
18.
Fellows, Michael R., Tobias Friedrich, Danny Hermelin, Nina Narodytska, & Frances Rosamond. (2012). Constraint satisfaction problems: Convexity makes AllDifferent constraints tractable. Theoretical Computer Science. 472. 81–89. 4 indexed citations
19.
Fellows, Michael R., Tobias Friedrich, Danny Hermelin, Nina Narodytska, & Frances Rosamond. (2011). Constraint satisfaction problems: convexity makes all different constraints tractable. Max Planck Institute for Plasma Physics. 522–527. 10 indexed citations
20.
Narodytska, Nina & Toby Walsh. (2007). Constraint and variable ordering heuristics for compiling configuration problems. International Joint Conference on Artificial Intelligence. 149–154. 17 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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