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.
Power law and exponential decay of inter contact times between mobile devices
2007355 citationsThomas Karagiannis, Jean‐Yves Le Boudec et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Milan Vojnović
Since
Specialization
Citations
This map shows the geographic impact of Milan Vojnović'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 Milan Vojnović with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milan Vojnović more than expected).
This network shows the impact of papers produced by Milan Vojnović. 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 Milan Vojnović. The network helps show where Milan Vojnović may publish in the future.
Co-authorship network of co-authors of Milan Vojnović
This figure shows the co-authorship network connecting the top 25 collaborators of Milan Vojnović.
A scholar is included among the top collaborators of Milan Vojnović 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 Milan Vojnović. Milan Vojnović is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Vojnović, Milan, et al.. (2020). Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models. International Conference on Artificial Intelligence and Statistics. 1254–1264.1 indexed citations
Alistarh, Dan, et al.. (2017). Communication-Efficient Stochastic Gradient Descent, with Applications to Neural Networks. London School of Economics and Political Science Research Online (London School of Economics and Political Science). 1669–1680.4 indexed citations
6.
Alistarh, Dan, et al.. (2016). QSGD: Communication-Optimal Stochastic Gradient Descent, with Applications to Training Neural Networks. arXiv (Cornell University).1 indexed citations
7.
Vojnović, Milan & Se-Young Yun. (2016). Parameter estimation for generalized thurstone choice models. London School of Economics and Political Science Research Online (London School of Economics and Political Science). 498–506.1 indexed citations
8.
Alistarh, Dan, Jerry Li, Ryota Tomioka, & Milan Vojnović. (2016). QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent. arXiv (Cornell University).35 indexed citations
9.
Alistarh, Dan, et al.. (2015). Streaming Min-max hypergraph partitioning. London School of Economics and Political Science Research Online (London School of Economics and Political Science). 28. 1900–1908.6 indexed citations
10.
Tsourakakis, Charalampos E., Christos Gkantsidis, Božidar Radunović, & Milan Vojnović. (2014). FENNEL. LSE Research Online. 333–342.222 indexed citations
11.
d’Aspremont, Alexandre, et al.. (2014). SerialRank: Spectral Ranking using Seriation. LSE Research Online. 27. 900–908.7 indexed citations
Karagiannis, Thomas, Jean‐Yves Le Boudec, & Milan Vojnović. (2007). Power law and exponential decay of inter contact times between mobile devices. 183–194.355 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.