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.
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
2024172 citationsLuca Longo, Mario Brčić et al.Information Fusionprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Gianclaudio Malgieri
Since
Specialization
Citations
This map shows the geographic impact of Gianclaudio Malgieri'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 Gianclaudio Malgieri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gianclaudio Malgieri more than expected).
Fields of papers citing papers by Gianclaudio Malgieri
This network shows the impact of papers produced by Gianclaudio Malgieri. 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 Gianclaudio Malgieri. The network helps show where Gianclaudio Malgieri may publish in the future.
Co-authorship network of co-authors of Gianclaudio Malgieri
This figure shows the co-authorship network connecting the top 25 collaborators of Gianclaudio Malgieri.
A scholar is included among the top collaborators of Gianclaudio Malgieri 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 Gianclaudio Malgieri. Gianclaudio Malgieri is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Longo, Luca, Mario Brčić, Federico Cabitza, et al.. (2024). Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion. 106. 102301–102301.172 indexed citations breakdown →
Ienca, Marcello & Gianclaudio Malgieri. (2022). Mental data protection and the GDPR. Journal of Law and the Biosciences. 9(1). lsac006–lsac006.25 indexed citations
Hamon, Ronan, H. Junklewitz, Gianclaudio Malgieri, et al.. (2021). Impossible Explanations? Beyond explainable AI in the GDPR from a COVID-19 Use Case Scenario. SSRN Electronic Journal.6 indexed citations
Malgieri, Gianclaudio. (2020). The Concept of Fairness in the GDPR: A Linguistic and Contextual Interpretation. SSRN Electronic Journal.3 indexed citations
Malgieri, Gianclaudio. (2019). Algorithmic Impact Assessments under the GDPR: Producing Multi-layered Explanations. VUBIR (Vrije Universiteit Brussel). 2019. 19–28.1 indexed citations
18.
Malgieri, Gianclaudio & Bart Custers. (2017). Pricing Privacy – The Right to Know the Value of Your Personal Data. SSRN Electronic Journal.8 indexed citations
19.
Malgieri, Gianclaudio. (2016). 'Ownership' of Customer (Big) Data in the European Union: Quasi-Property as Comparative Solution?. SSRN Electronic Journal. 20(5). 2–17.1 indexed citations
20.
Malgieri, Gianclaudio. (2016). Trade Secrets v Personal Data: A Possible Solution for Balancing Rights. SSRN Electronic Journal.2 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.