Rocco Langone

1.0k total citations
38 papers, 721 citations indexed

About

Rocco Langone is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Rocco Langone has authored 38 papers receiving a total of 721 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 16 papers in Computer Vision and Pattern Recognition and 15 papers in Statistical and Nonlinear Physics. Recurrent topics in Rocco Langone's work include Advanced Clustering Algorithms Research (18 papers), Complex Network Analysis Techniques (14 papers) and Face and Expression Recognition (13 papers). Rocco Langone is often cited by papers focused on Advanced Clustering Algorithms Research (18 papers), Complex Network Analysis Techniques (14 papers) and Face and Expression Recognition (13 papers). Rocco Langone collaborates with scholars based in Belgium, Italy and Ireland. Rocco Langone's co-authors include Johan A. K. Suykens, Raghvendra Mall, Carlos Alzate, Siamak Mehrkanoon, Antonello Pasini, Edwin Reynders, Alfredo Cuzzocrea, Bart De Ketelaere, Wannes Meert and Bart De Moor and has published in prestigious journals such as PLoS ONE, Journal of Climate and Information Sciences.

In The Last Decade

Rocco Langone

38 papers receiving 702 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rocco Langone Belgium 15 338 255 112 89 89 38 721
Chunping Wang China 16 218 0.6× 225 0.9× 39 0.3× 59 0.7× 95 1.1× 77 890
Zixing Song Hong Kong 10 492 1.5× 255 1.0× 41 0.4× 38 0.4× 47 0.5× 15 879
Grigorios Tzortzis Greece 9 425 1.3× 386 1.5× 66 0.6× 31 0.3× 70 0.8× 11 717
Xuming Han China 12 311 0.9× 181 0.7× 34 0.3× 40 0.4× 31 0.3× 64 744
Riadh Ksantini Canada 13 242 0.7× 200 0.8× 44 0.4× 50 0.6× 31 0.3× 72 568
Arnaud Martin France 15 474 1.4× 120 0.5× 63 0.6× 77 0.9× 38 0.4× 51 881
Guoqing Chao China 15 567 1.7× 611 2.4× 43 0.4× 34 0.4× 118 1.3× 35 975
Xiangli Yang China 3 322 1.0× 215 0.8× 20 0.2× 37 0.4× 43 0.5× 7 670
Gwang-Hoon Park South Korea 3 603 1.8× 254 1.0× 25 0.2× 125 1.4× 22 0.2× 4 841
Jakob Gawlikowski Germany 7 243 0.7× 101 0.4× 20 0.2× 78 0.9× 52 0.6× 13 633

Countries citing papers authored by Rocco Langone

Since Specialization
Citations

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

Fields of papers citing papers by Rocco Langone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rocco Langone

This figure shows the co-authorship network connecting the top 25 collaborators of Rocco Langone. A scholar is included among the top collaborators of Rocco Langone 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 Rocco Langone. Rocco Langone 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.
Langone, Rocco & Johan A. K. Suykens. (2017). Fast kernel spectral clustering. Neurocomputing. 268. 27–33. 16 indexed citations
2.
Langone, Rocco, et al.. (2017). Multi-View Least Squares Support Vector Machines Classification. Neurocomputing. 282. 78–88. 62 indexed citations
3.
Langone, Rocco, Marc Van Barel, & Johan A. K. Suykens. (2016). Entropy-Based Incomplete Cholesky Decomposition for a Scalable Spectral Clustering Algorithm: Computational Studies and Sensitivity Analysis. Entropy. 18(5). 182–182. 5 indexed citations
4.
Langone, Rocco & Johan A. K. Suykens. (2016). Supervised aggregated feature learning for multiple instance classification. Information Sciences. 375. 234–245. 3 indexed citations
5.
Langone, Rocco, Edwin Reynders, Siamak Mehrkanoon, & Johan A. K. Suykens. (2016). Automated structural health monitoring based on adaptive kernel spectral clustering. Mechanical Systems and Signal Processing. 90. 64–78. 69 indexed citations
6.
Mall, Raghvendra, Rocco Langone, & Johan A. K. Suykens. (2015). Netgram: Visualizing Communities in Evolving Networks. PLoS ONE. 10(9). e0137502–e0137502. 6 indexed citations
7.
Mall, Raghvendra, Rocco Langone, & Johan A. K. Suykens. (2014). Agglomerative hierarchical kernel spectral clustering for large scale networks. The European Symposium on Artificial Neural Networks. 1–6. 1 indexed citations
8.
Mall, Raghvendra, Rocco Langone, & Johan A. K. Suykens. (2014). Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks. PLoS ONE. 9(6). e99966–e99966. 28 indexed citations
9.
Mall, Raghvendra, Siamak Mehrkanoon, Rocco Langone, & Johan A. K. Suykens. (2014). Optimal reduced sets for sparse kernel spectral clustering. Lirias (KU Leuven). 3. 2436–2443. 5 indexed citations
10.
Langone, Rocco, et al.. (2014). LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. Engineering Applications of Artificial Intelligence. 37. 268–278. 67 indexed citations
11.
Langone, Rocco, et al.. (2014). Alarm prediction in industrial machines using autoregressive LS-SVM models. Lirias (KU Leuven). 359–364. 6 indexed citations
12.
Mall, Raghvendra, Rocco Langone, & Johan A. K. Suykens. (2014). Agglomerative hierarchical kernel spectral data clustering. Lirias (KU Leuven). 22. 9–16. 4 indexed citations
13.
Langone, Rocco, Raghvendra Mall, & Johan A. K. Suykens. (2014). Clustering data over time using kernel spectral clustering with memory. Lirias (KU Leuven). 1–8. 9 indexed citations
14.
Mall, Raghvendra, et al.. (2014). Representative Subsets For Big Data Learning using k-NN Graphs. 37–42. 1 indexed citations
15.
Langone, Rocco, Carlos Alzate, & Johan A. K. Suykens. (2013). Kernel spectral clustering with memory effect. Physica A Statistical Mechanics and its Applications. 392(10). 2588–2606. 14 indexed citations
16.
Mall, Raghvendra, Rocco Langone, & Johan A. K. Suykens. (2013). Self-tuned kernel spectral clustering for large scale networks. Lirias (KU Leuven). 385–393. 21 indexed citations
17.
Mall, Raghvendra, Rocco Langone, & Johan A. K. Suykens. (2013). FURS: Fast and Unique Representative Subset selection retaining large-scale community structure. Social Network Analysis and Mining. 3(4). 1075–1095. 20 indexed citations
18.
Pasini, Antonello & Rocco Langone. (2012). Influence of Circulation Patterns on Temperature Behavior at the Regional Scale: A Case Study Investigated via Neural Network Modeling. Journal of Climate. 25(6). 2123–2128. 18 indexed citations
19.
Pasini, Antonello, et al.. (2010). Energy-based predictions in Lorenz system by a unified formalism and neural network modelling. Nonlinear processes in geophysics. 17(6). 809–815. 5 indexed citations
20.
Pasini, Antonello, et al.. (2009). Assessing Climatic Influences on Rodent Density. Asia-Pacific Journal of Atmospheric Sciences. 45(3). 319–330. 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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