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.
Cybersecurity data science: an overview from machine learning perspective
2020366 citationsIqbal H. Sarker, A. S. M. Kayes et al.Journal Of Big Dataprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Paul Watters'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 Paul Watters with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul Watters more than expected).
This network shows the impact of papers produced by Paul Watters. 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 Paul Watters. The network helps show where Paul Watters may publish in the future.
Co-authorship network of co-authors of Paul Watters
This figure shows the co-authorship network connecting the top 25 collaborators of Paul Watters.
A scholar is included among the top collaborators of Paul Watters 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 Paul Watters. Paul Watters is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sarker, Iqbal H., A. S. M. Kayes, Shahriar Badsha, et al.. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal Of Big Data. 7(1).366 indexed citations breakdown →
Suriadi, Suriadi, et al.. (2016). Characterizing Problem Gamblers in New Zealand: A Novel Expression of Process Cubes.. QUT ePrints (Queensland University of Technology). 185–192.1 indexed citations
9.
Layton, Robert, Paul Watters, & Richard Dazeley. (2013). Local n-grams for author identification: notebook for PAN at CLEF 2013. Own your potential (DEAKIN). 1–4.4 indexed citations
10.
Alazab, Mamoun, Sitalakshmi Venkatraman, Paul Watters, Moutaz Alazab, & Ammar Alazab. (2011). Cybercrime : the case of obfuscated malware. Deakin Research Online (Deakin University).6 indexed citations
11.
Alazab, Mamoun, Sitalakshmi Venkatraman, Paul Watters, & Moutaz Alazab. (2011). Zero-day malware detection based on supervised learning algorithms of API call signatures. FedUni ResearchOnline (Federation University Australia). 171–182.118 indexed citations
12.
Land, Lesley Pek Wee, et al.. (2010). Descriptive data mining on fraudulent online dating profiles. Journal of the Association for Information Systems. 145.5 indexed citations
13.
Watters, Paul & Margaret O’Mahony. (2007). The relationship between geometric design consistency and safety on rural single carriageways in Ireland.8 indexed citations
14.
O’Mahony, Margaret, Brian Caulfield, & Paul Watters. (2006). Response to Cash-Outs for Work Place Parking. Transportation Research Board 85th Annual MeetingTransportation Research Board.1 indexed citations
Watters, Paul, et al.. (1996). Social processes as dynamical processes: Qualitative dynamical systems theory in social psychology. eCite Digital Repository (University of Tasmania).9 indexed citations
20.
Snelson, A., et al.. (1992). THE JOURNEY TO WORK: REPORTS AND STATED PREFERENCE OF 2520 AUTOMOBILE ASSOCIATION EMPLOYEES AND THEIR RESPONSE TO A CAR-SHARING INITIATIVE. Traffic engineering & control. 33(11). 619–623.1 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.