Natali Ruchansky

677 total citations · 1 hit paper
9 papers, 381 citations indexed

About

Natali Ruchansky is a scholar working on Artificial Intelligence, Information Systems and Signal Processing. According to data from OpenAlex, Natali Ruchansky has authored 9 papers receiving a total of 381 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Information Systems and 3 papers in Signal Processing. Recurrent topics in Natali Ruchansky's work include Sparse and Compressive Sensing Techniques (2 papers), Spam and Phishing Detection (2 papers) and Misinformation and Its Impacts (2 papers). Natali Ruchansky is often cited by papers focused on Sparse and Compressive Sensing Techniques (2 papers), Spam and Phishing Detection (2 papers) and Misinformation and Its Impacts (2 papers). Natali Ruchansky collaborates with scholars based in United States, Italy and China. Natali Ruchansky's co-authors include Yan Liu, Karishma Sharma, Feng Qian, He Jiang, Ming Zhang, Davide Proserpio, Evimaria Terzi, Mark Crovella, Sungyong Seo and Gianmarco De Francisci Morales and has published in prestigious journals such as ACM SIGCOMM Computer Communication Review, Lecture notes in computer science and ACM Transactions on Intelligent Systems and Technology.

In The Last Decade

Natali Ruchansky

9 papers receiving 364 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Combating Fake News

2019 318 citations Karishma Sharma, Feng Qian et al. ACM Transactions on Intelligent Systems and Technology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Natali Ruchansky United States	5	296	204	174	62	57	9	381
Alessandro Bondielli Italy	5	367 1.2×	281 1.4×	226 1.3×	82 1.3×	80 1.4×	14	462
Prashant Shiralkar United States	6	218 0.7×	151 0.7×	214 1.2×	44 0.7×	42 0.7×	10	369
Muhammad Ahmad Bashir United States	8	120 0.4×	185 0.9×	151 0.9×	41 0.7×	72 1.3×	10	306
Pierre‐Antoine Vervier United States	7	94 0.3×	106 0.5×	136 0.8×	17 0.3×	82 1.4×	11	281
Shirin Nilizadeh United States	9	168 0.6×	123 0.6×	291 1.7×	29 0.5×	45 0.8×	27	429
Elena Kochkina United Kingdom	8	319 1.1×	181 0.9×	351 2.0×	102 1.6×	31 0.5×	16	474
Wanjun Zhong China	11	156 0.5×	155 0.8×	411 2.4×	24 0.4×	23 0.4×	36	543
Sourya Kakarla India	3	245 0.8×	135 0.7×	144 0.8×	30 0.5×	39 0.7×	3	314
Robert Escriva United States	6	87 0.3×	219 1.1×	90 0.5×	41 0.7×	17 0.3×	7	385
Christina Boididou United Kingdom	9	359 1.2×	240 1.2×	223 1.3×	57 0.9×	90 1.6×	12	451

Countries citing papers authored by Natali Ruchansky

Since Specialization

Citations

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

Fields of papers citing papers by Natali Ruchansky

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Natali Ruchansky

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Morales, Gianmarco De Francisci, Claudia Perlich, Natali Ruchansky, et al.. (2023). Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. Lecture notes in computer science. 3 indexed citations

2.

Sharma, Karishma, Feng Qian, He Jiang, et al.. (2019). Combating Fake News. ACM Transactions on Intelligent Systems and Technology. 10(3). 1–42. 318 indexed citations breakdown →

3.

Ruchansky, Natali, Sungyong Seo, & Yan Liu. (2017). CSI: A Hybrid Deep Model for Fake News.. arXiv (Cornell University). 16 indexed citations

4.

Ruchansky, Natali & Davide Proserpio. (2013). A (not) NICE way to verify the openflow switch specification. 527–528. 16 indexed citations

5.

Ruchansky, Natali & Davide Proserpio. (2013). A (not) NICE way to verify the openflow switch specification. ACM SIGCOMM Computer Communication Review. 43(4). 527–528. 4 indexed citations

6.

Ruchansky, Natali, et al.. (2012). Inferring visibility. ACM SIGCOMM Computer Communication Review. 42(4). 151–162. 4 indexed citations

7.

Ruchansky, Natali, et al.. (2012). Routing state distance. 239–252. 9 indexed citations

8.

Ruchansky, Natali, et al.. (2012). Inferring visibility. 151–162. 7 indexed citations

9.

Ruchansky, Natali, Christine Löchner, Ellen Yi–Luen, et al.. (2011). Monitoring workspace activities using accelerometers. 1837–1839. 4 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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