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.
Semantics derived automatically from language corpora contain human-like biases
20171.4k citationsArvind Narayanan et al.profile →
Countries citing papers authored by Arvind Narayanan
Since
Specialization
Citations
This map shows the geographic impact of Arvind Narayanan'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 Arvind Narayanan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arvind Narayanan more than expected).
Fields of papers citing papers by Arvind Narayanan
This network shows the impact of papers produced by Arvind Narayanan. 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 Arvind Narayanan. The network helps show where Arvind Narayanan may publish in the future.
Co-authorship network of co-authors of Arvind Narayanan
This figure shows the co-authorship network connecting the top 25 collaborators of Arvind Narayanan.
A scholar is included among the top collaborators of Arvind Narayanan 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 Arvind Narayanan. Arvind Narayanan 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.
Narayanan, Arvind, et al.. (2025). AI Snake Oil. Princeton University Press eBooks.
Lee, Kevin, et al.. (2020). An Empirical Study of Wireless Carrier Authentication for SIM Swaps.. Symposium On Usable Privacy and Security. 61–79.11 indexed citations
Mathur, Arunesh, Jessica Vitak, Arvind Narayanan, & Marshini Chetty. (2018). Characterizing the Use of Browser-Based Blocking Extensions To Prevent Online Tracking.. Symposium On Usable Privacy and Security. 103–116.25 indexed citations
14.
Bailis, Peter, Arvind Narayanan, Andrew Miller, & Song Han. (2016). Cryptocurrencies, blockchains, and smart contracts; Hardware for deep learning. Queue. 14(6).1 indexed citations
15.
Harang, Richard, Andy Liu, Arvind Narayanan, et al.. (2015). De-anonymizing programmers via code stylometry. USENIX Security Symposium. 255–270.97 indexed citations
16.
Vallor, Shannon & Arvind Narayanan. (2013). An Introduction to Software Engineering Ethics. 91(6). 37–40.7 indexed citations
Kalodner, Harry, et al.. (2011). An empirical study of Namecoin and lessons for decentralized namespace design.98 indexed citations
19.
Narayanan, Arvind, et al.. (2011). Location Privacy via Private Proximity Testing.. Network and Distributed System Security Symposium.201 indexed citations
20.
Narayanan, Arvind & Vitaly Shmatikov. (2008). Robust De-anonymization of Large Sparse Datasets. 111–125.1304 indexed citations breakdown →
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.