Alessandro Sperduti

162 papers receiving 2.7k citations

Peers

Alessandro Sperduti
Comparison fields: 5 of 143
  • Artificial Intelligence 1.8k
  • Computer Vision and Pattern Recognition 658
  • Information Systems 381
  • Computational Theory and Mathematics 326
  • Signal Processing 310
Replace Xiaoyong Du with:
Xiaoyong Du China
Ran El‐Yaniv Israel
Martin Zinkevich United States
Stefan Wrobel Germany
Geoffrey J. Gordon United States
Pradeep Ravikumar United States
Daniel Golovin United States
Ankur Dave United States
Lei Li China
Xiangrui Meng China
Alessandro Sperduti relative to Xiaoyong Du China Xiaoyong Du's profile →
Citations per field
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Xiaoyong Du · 1×
Citations per year

Countries citing papers authored by Alessandro Sperduti

Since Specialization
Citations

This map shows the geographic impact of Alessandro Sperduti'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 Alessandro Sperduti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alessandro Sperduti more than expected).

Fields of papers citing papers by Alessandro Sperduti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alessandro Sperduti. 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 Alessandro Sperduti. The network helps show where Alessandro Sperduti may publish in the future.

Co-authorship network of co-authors of Alessandro Sperduti

This figure shows the co-authorship network connecting the top 25 collaborators of Alessandro Sperduti. A scholar is included among the top collaborators of Alessandro Sperduti 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 Alessandro Sperduti. Alessandro Sperduti 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
#WorkIndexed citations
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The Conjunctive Disjunctive Node Kernel
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Equivalence results between feedforward and recurrent neural networks for sequences
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A lossy counting based approach for learning on streams of graphs on a budget
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Projection of undirected and non-positional graphs using self organizing maps
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A Note on Formal Determination of Context in Contextual Recursive Cascade Correlation Networks
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Dynamical Neural Networks Construction for Processing of Labeled Structures
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About Alessandro Sperduti

Alessandro Sperduti is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 176 papers that have together received 2.8k indexed citations. Recurring topics across this work include Neural Networks and Applications (67 papers), Advanced Graph Neural Networks (29 papers) and Face and Expression Recognition (23 papers). The work is most often cited by research in Artificial Intelligence (1.8k citations), Management Information Systems (309 citations) and Computer Vision and Pattern Recognition (658 citations). Alessandro Sperduti has collaborated with scholars based in Italy, Germany and United States. Frequent co-authors include Antonina Starita, Alessio Micheli, Marco Gori, Andrea Burattin, Paolo Frasconi, Nicolò Navarin, Fabio Aiolli, Markus Hagenbuchner, Giovanni Da San Martino and Barbara Hammer. Their work appears in journals such as Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence and International Journal of Molecular Sciences.

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.

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