Countries citing papers authored by Flavio Chierichetti
Since
Specialization
Citations
This map shows the geographic impact of Flavio Chierichetti'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 Flavio Chierichetti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Flavio Chierichetti more than expected).
Fields of papers citing papers by Flavio Chierichetti
This network shows the impact of papers produced by Flavio Chierichetti. 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 Flavio Chierichetti. The network helps show where Flavio Chierichetti may publish in the future.
Co-authorship network of co-authors of Flavio Chierichetti
This figure shows the co-authorship network connecting the top 25 collaborators of Flavio Chierichetti.
A scholar is included among the top collaborators of Flavio Chierichetti 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 Flavio Chierichetti. Flavio Chierichetti 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.
Chierichetti, Flavio, et al.. (2021). Online Facility Location with Multiple Advice. IRIS Research product catalog (Sapienza University of Rome). 34.1 indexed citations
2.
Chierichetti, Flavio, Ravi Kumar, & Andrew Tomkins. (2021). Learning a Mixture of Two Multinomial Logits. IRIS Research product catalog (Sapienza University of Rome). 960–968.2 indexed citations
3.
Chierichetti, Flavio, Ravi Kumar, Silvio Lattanzi, & Sergei Vassilvitskii. (2021). Matroids, Matchings and Fairness. Zenodo (CERN European Organization for Nuclear Research). 2212–2220.7 indexed citations
4.
Chierichetti, Flavio, Jon Kleinberg, & Sigal Oren. (2017). On discrete preferences and coordination. Journal of Computer and System Sciences. 93. 11–29.6 indexed citations
5.
Chierichetti, Flavio, Sreenivas Gollapudi, Ravi Kumar, et al.. (2017). Algorithms for ℓp Low Rank Approximation.1 indexed citations
6.
Baeza‐Yates, Ricardo, Paolo Boldi, & Flavio Chierichetti. (2015). Essential Web Pages Are Easy to Find. IRIS Research product catalog (Sapienza University of Rome). 97–107.2 indexed citations
7.
Chierichetti, Flavio, Anirban Dasgupta, Ravi Kumar, & Silvio Lattanzi. (2015). On Learning Mixture Models for Permutations. IRIS Research product catalog (Sapienza University of Rome). 85–92.7 indexed citations
8.
Chierichetti, Flavio, Ravi Kumar, & Mohammad Mahdian. (2014). The complexity of LSH feasibility. Theoretical Computer Science. 530. 89–101.4 indexed citations
9.
Chierichetti, Flavio, Anirban Dasgupta, Ravi Kumar, & Silvio Lattanzi. (2014). On Reconstructing a Hidden Permutation. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 28. 617.7 indexed citations
Chierichetti, Flavio, et al.. (2012). Are web users really Markovian?. IRIS Research product catalog (Sapienza University of Rome). 609–618.75 indexed citations
13.
Chierichetti, Flavio, David Liben‐Nowell, & Jon Kleinberg. (2011). Reconstructing Patterns of Information Diffusion from Incomplete Observations. IRIS Research product catalog (Sapienza University of Rome). 24. 792–800.11 indexed citations
Chierichetti, Flavio, Ravi Kumar, & Andrew Tomkins. (2010). Stochastic models for tabbed browsing. IRIS Research product catalog (Sapienza University of Rome). 241–250.9 indexed citations
20.
Chierichetti, Flavio, Ravi Kumar, & Andrew Tomkins. (2010). Max-cover in map-reduce. IRIS Research product catalog (Sapienza University of Rome). 231–240.65 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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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.