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.
Flow Monitoring Explained: From Packet Capture to Data Analysis With NetFlow and IPFIX
2014292 citationsRick Hofstede, Pavel Čeleda et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
hero ref
This map shows the geographic impact of Pavel Čeleda'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 Pavel Čeleda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavel Čeleda more than expected).
This network shows the impact of papers produced by Pavel Čeleda. 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 Pavel Čeleda. The network helps show where Pavel Čeleda may publish in the future.
Co-authorship network of co-authors of Pavel Čeleda
This figure shows the co-authorship network connecting the top 25 collaborators of Pavel Čeleda.
A scholar is included among the top collaborators of Pavel Čeleda 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 Pavel Čeleda. Pavel Čeleda is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Velan, Petr & Pavel Čeleda. (2019). Application-Aware Flow Monitoring. Immunotechnology. 701–706.1 indexed citations
12.
Jirsík, Tomáš, et al.. (2019). Quality of Service Forecasting with LSTM Neural Network. Immunotechnology. 251–260.7 indexed citations
13.
Vykopal, Jan, et al.. (2017). KYPO Cyber Range: Design and Use Cases. Veřejné služby Informačního systému (Masarykiana Brunensis Universitas). 310–321.48 indexed citations
Čermák, Milan, et al.. (2016). A Performance Benchmark of NetFlow Data Analysis on Distributed Stream Processing Systems. Veřejné služby Informačního systému (Masarykiana Brunensis Universitas).13 indexed citations
16.
Čeleda, Pavel, et al.. (2013). Large-scale geolocation for NetFlow. University of Twente Research Information. 1015–1020.1 indexed citations
17.
Doyen, Guillaume, et al.. (2013). Proceedings of the 7th IFIP WG 6.6 international conference on Autonomous Infrastructure, Management, and Security: emerging management mechanisms for the future internet - Volume 7943.1 indexed citations
18.
Čeleda, Pavel, et al.. (2011). Monitoring of tunneled IPv6 traffic using packet decapsulation and IPFIX. 64–71.1 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.