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.
Deep learning for deepfakes creation and detection: A survey
2022216 citationsThanh Thi Nguyen, Thanh Thi Nguyen et al.Computer Vision and Image Understandingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Dung T. Nguyen
Since
Specialization
Citations
This map shows the geographic impact of Dung T. Nguyen'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 Dung T. Nguyen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dung T. Nguyen more than expected).
This network shows the impact of papers produced by Dung T. Nguyen. 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 Dung T. Nguyen. The network helps show where Dung T. Nguyen may publish in the future.
Co-authorship network of co-authors of Dung T. Nguyen
This figure shows the co-authorship network connecting the top 25 collaborators of Dung T. Nguyen.
A scholar is included among the top collaborators of Dung T. Nguyen 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 Dung T. Nguyen. Dung T. Nguyen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nguyen, Dung T. & Thanh Quang Ngo. (2019). Dynamics of Household-level Energy Access in Vietnam during 2002-2014. SHILAP Revista de lepidopterología.3 indexed citations
13.
Nguyen, Thanh Thi, Cuong Nguyen, Dung T. Nguyen, Duc Thanh Nguyen, & Saeid Nahavandi. (2019). Deep Learning for Deepfakes Creation and Detection.. arXiv (Cornell University).57 indexed citations
14.
He, Liang, et al.. (2015). CozyMaps. 46–51.4 indexed citations
15.
Nguyen, Dung T. & Alan L. Selman. (2014). Non-autoreducible Sets for NEXP. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 590–601.2 indexed citations
Ngo, Hung Q., Dung T. Nguyen, Christopher Ré, & Atri Rudra. (2013). Towards Instance Optimal Join Algorithms for Data in Indexes. arXiv (Cornell University).1 indexed citations
18.
Ngo, Hung Q., Dung T. Nguyen, Christopher Ré, & Atri Rudra. (2013). Removing the Haystack to Find the Needle(s): Minesweeper, an adaptive join algorithm. arXiv (Cornell University).1 indexed citations
Do, Thanh, et al.. (2009). Failure-aware Scheduling in Grid Computing Environments.. 5(1). 40–46.10 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.