M. Vento

3.2k total citations · 1 hit paper
24 papers, 1.7k citations indexed

About

M. Vento is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, M. Vento has authored 24 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 7 papers in Signal Processing. Recurrent topics in M. Vento's work include Graph Theory and Algorithms (12 papers), Data Management and Algorithms (7 papers) and Handwritten Text Recognition Techniques (4 papers). M. Vento is often cited by papers focused on Graph Theory and Algorithms (12 papers), Data Management and Algorithms (7 papers) and Handwritten Text Recognition Techniques (4 papers). M. Vento collaborates with scholars based in Italy, Switzerland and France. M. Vento's co-authors include Carlo Sansone, Pasquale Foggia, L.P. Cordella, Claudio De Stefano, Donatello Conte, Massimo De Santo, Nicola Capuano, Luca Greco, Pierluigi Ritrovato and Walter G. Kropatsch and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

M. Vento

24 papers receiving 1.6k citations

Hit Papers

A (sub)graph isomorphism algorithm for matching large graphs 2004 2026 2011 2018 2004 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
M. Vento Italy 13 909 745 403 346 228 24 1.7k
L.P. Cordella Italy 17 1.2k 1.3× 739 1.0× 399 1.0× 319 0.9× 227 1.0× 53 1.9k
Alexandr Andoni United States 17 1.5k 1.6× 1.0k 1.3× 453 1.1× 368 1.1× 252 1.1× 57 2.4k
J. R. Ullmann United Kingdom 8 888 1.0× 638 0.9× 438 1.1× 357 1.0× 235 1.0× 30 1.6k
Hisao Tamaki Japan 16 359 0.4× 880 1.2× 192 0.5× 307 0.9× 472 2.1× 62 1.8k
Renzo Angles Chile 12 876 1.0× 640 0.9× 340 0.8× 670 1.9× 97 0.4× 32 1.6k
Wook-Shin Han South Korea 22 1.2k 1.3× 1.1k 1.5× 725 1.8× 654 1.9× 120 0.5× 101 2.0k
Kai Lü China 19 352 0.4× 1.2k 1.6× 160 0.4× 323 0.9× 434 1.9× 130 1.7k
Rasmus Pagh Denmark 21 515 0.6× 1.3k 1.7× 290 0.7× 1.2k 3.4× 387 1.7× 88 2.3k
Bin Shao China 16 711 0.8× 564 0.8× 248 0.6× 478 1.4× 218 1.0× 40 1.4k
Fabrizio Luccio Italy 19 201 0.2× 586 0.8× 379 0.9× 559 1.6× 580 2.5× 102 1.7k

Countries citing papers authored by M. Vento

Since Specialization
Citations

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

Fields of papers citing papers by M. Vento

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Vento

This figure shows the co-authorship network connecting the top 25 collaborators of M. Vento. A scholar is included among the top collaborators of M. Vento 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 M. Vento. M. Vento 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.
Vento, M.. (2014). A long trip in the charming world of graphs for Pattern Recognition. Pattern Recognition. 48(2). 291–301. 63 indexed citations
2.
Colace, Francesco, Massimo De Santo, & M. Vento. (2010). A MultiExpert Approach for Bayesian Network Structural Learning. 1–11. 6 indexed citations
3.
Brun, Luc, et al.. (2010). Symbolic Learning vs. Graph Kernels: An Experimental Comparison in a Chemical Application.. 31–40. 8 indexed citations
4.
Conte, Donatello, et al.. (2009). EVALUATION AND IMPROVEMENTS OF THE LEVEL SET METHOD FOR RM IMAGES SEGMENTATION. 210–215. 1 indexed citations
5.
Conte, Donatello, Pasquale Foggia, & M. Vento. (2007). Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs. Journal of Graph Algorithms and Applications. 11(1). 99–143. 58 indexed citations
6.
Brun, Luc & M. Vento. (2005). Graph-Based Representations in Pattern Recognition: 5th IAPR International Workshop, GbRPR 2005, Poitiers, France, April 11-13, 2005, Proceedings (Lecture Notes in Computer Science). Springer eBooks. 3 indexed citations
7.
Cordella, L.P., Pasquale Foggia, Carlo Sansone, & M. Vento. (2004). A (sub)graph isomorphism algorithm for matching large graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 26(10). 1367–1372. 874 indexed citations breakdown →
8.
Bunke, Horst, et al.. (2004). Weighted minimum common supergraph for cluster representation. Zenodo (CERN European Organization for Nuclear Research). 3. II–25. 4 indexed citations
9.
Cordella, L.P., Pasquale Foggia, Carlo Sansone, & M. Vento. (2002). Fast graph matching for detecting CAD image components. 2. 1034–1037. 15 indexed citations
10.
Cordella, L.P., Pasquale Foggia, Carlo Sansone, Francesco Tortorella, & M. Vento. (2002). Graph matching: a fast algorithm and its evaluation. 2. 1582–1584. 20 indexed citations
11.
Cordella, L.P., Pasquale Foggia, Carlo Sansone, & M. Vento. (2002). Learning structural shape descriptions from examples. Pattern Recognition Letters. 23(12). 1427–1437. 11 indexed citations
12.
Foggia, Pasquale, et al.. (2001). Symbolic vs. connectionist learning: an experimental comparison in a structured domain. IEEE Transactions on Knowledge and Data Engineering. 13(2). 176–195. 8 indexed citations
13.
Foggia, Pasquale, Carlo Sansone, & M. Vento. (2001). A Performance Comparison of Five Algorithms for Graph Isomorphism. 76 indexed citations
14.
Jolion, Jean-Michel, Walter G. Kropatsch, & M. Vento. (2001). 3rd IAPR TC-15 Workshop on Graph-based Representations in Pattern Recognition. 25 indexed citations
15.
Cordella, L.P., Pasquale Foggia, Carlo Sansone, & M. Vento. (2001). An Improved Algorithm for Matching Large Graphs. 200 indexed citations
16.
Cordella, L.P. & M. Vento. (2000). Symbol recognition in documents: a collection of techniques?. International Journal on Document Analysis and Recognition (IJDAR). 3(2). 73–88. 50 indexed citations
17.
Stefano, Claudio De, Carlo Sansone, & M. Vento. (2000). To reject or not to reject: that is the question-an answer in case of neural classifiers. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews). 30(1). 84–94. 125 indexed citations
18.
Cordella, L.P., Pasquale Foggia, Carlo Sansone, & M. Vento. (1999). Document validation by signature: a serial multi-expert approach. 601–604. 9 indexed citations
19.
Cordella, L.P., Pasquale Foggia, Carlo Sansone, & M. Vento. (1996). An efficient algorithm for the inexact matching of ARG graphs using a contextual transformational model. 180–184 vol.3. 17 indexed citations
20.
Stefano, Claudio De, Carlo Sansone, & M. Vento. (1995). Comparing Generalization and Recognition Capability of Learning Vector Quantization and Multi-layer Perceptron Architectures. 1123–1130. 5 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.

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