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.
Dissecting Ponzi schemes on Ethereum: identification, analysis, and\n impact
2017209 citationsSalvatore Carta, Roberto Saia 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 Salvatore Carta
Since
Specialization
Citations
This map shows the geographic impact of Salvatore Carta'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 Salvatore Carta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Salvatore Carta more than expected).
This network shows the impact of papers produced by Salvatore Carta. 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 Salvatore Carta. The network helps show where Salvatore Carta may publish in the future.
Co-authorship network of co-authors of Salvatore Carta
This figure shows the co-authorship network connecting the top 25 collaborators of Salvatore Carta.
A scholar is included among the top collaborators of Salvatore Carta 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 Salvatore Carta. Salvatore Carta is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Armano, Giuliano, Sebastiano Battiato, Ludovico Boratto, et al.. (2018). NewsVallum: Semantics-Aware Text and Image Processing for Fake News Detection system.. UNICA IRIS Institutional Research Information System (University of Cagliari).1 indexed citations
11.
Boratto, Ludovico, et al.. (2018). Predicting workout quality to help coaches support sportspeople. Conference on Recommender Systems. 2216. 8–12.7 indexed citations
12.
Boratto, Ludovico, et al.. (2017). Recommendation in Persuasive eHealth Systems: an Effective Strategy to Spot Users' Losing Motivation to Exercise.. UNICA IRIS Institutional Research Information System (University of Cagliari). 1953. 6–9.6 indexed citations
13.
Manca, M., Ludovico Boratto, & Salvatore Carta. (2013). Producing Friend Recommendations in a Social Bookmarking System by Mining Users Content. UNICA IRIS Institutional Research Information System (University of Cagliari). 59–64.3 indexed citations
Boratto, Ludovico & Salvatore Carta. (2013). Exploring the Ratings Prediction Task in a Group Recommender System that Automatically Detects Groups. UNICA IRIS Institutional Research Information System (University of Cagliari). 36–43.3 indexed citations
16.
Boratto, Ludovico, et al.. (2010). Groups Identification and Individual Recommendations in Group Recommendation Algorithms.. UNICA IRIS Institutional Research Information System (University of Cagliari). 27–34.23 indexed citations
Acquaviva, Andrea, et al.. (2006). A Control Theoretic Approach to Run-Time Energy Optimization of Pipelined Elaboration in MPSoCs. UNICA IRIS Institutional Research Information System (University of Cagliari). 1. 1–2.3 indexed citations
19.
Stergiou, Stergios, Federico Angiolini, Salvatore Carta, et al.. (2005). ×pipes Lite: A Synthesis Oriented Design Library For Networks on Chips. SPIRE - Sciences Po Institutional REpository.
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.