Publications
This page presents a selection of my most relevant publications, organized by research area.
For a complete list, please visit my Google Scholar profile.
Monographs
- Pagliara F., Aria M. & Mauriello M. (2025). Models and Applications of Tourists’ Travel Behavior. 1st Edition, Elsevier, ISBN: 9780443265938.
- Cuccurullo C., Aria M., Spano M. & D’Aniello L. (2023). Leading Change in Academic Health Science Centers. Zaccaria ed., ISBN: 9788899594213.
Journal Articles
Bibliometrics and Science Mapping
Aria, M., Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975, DOI: 10.1016/j.joi.2017.08.007.
Aria M., Angelelli M., Ciavolino E., Ringle, C.M., Sarstedt M. (2025). Conceptual structure and thematic evolution in partial least squares structural equation modeling research. Quality & Quantity, DOI: 10.1007/s11135-025-02071-4.
Aria, M., Cuccurullo, C., D’Aniello, L., Misuraca, M., & Spano, M. (2024). Comparative science mapping: a novel conceptual structure analysis with metadata. Scientometrics, DOI: 10.1007/s11192-024-05161-6.
Aria, M., Le, T., Cuccurullo, C., Belfiore, A., & Choe, J. (2024). openalexR: An R-Tool for Collecting Bibliometric Data from OpenAlex. The R Journal, DOI: 10.32614/RJ-2023-089.
Ciavolino, E., Aria, M., Cheah, J. H., & Roldán, J. L. (2022). A tale of PLS Structural Equation Modelling: Episode I— A Bibliometrix Citation Analysis. Social Indicators Research, DOI: 10.1007/s11205-022-02994-7.
D’Aniello L., Spano M., Cuccurullo C., Aria M. (2022). Academic Health Centers’ configurations, scientific productivity, and impact: Insights from the Italian setting. Health Policy, DOI: 10.1016/j.healthpol.2022.09.007.
Belfiore, A., Cuccurullo, C., & Aria, M. (2022). IoT in healthcare: A scientometric analysis. Technological Forecasting and Social Change, 184, DOI: 10.1016/j.techfore.2022.122001.
Aria M., Misuraca M., Spano M. (2020). Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research. Social Indicators Research, DOI: 10.1007/s11205-020-02281-3.
Statistics and Machine Learning
Aria M., Gnasso A., Iorio C., Pandolfo, G. (2023). Explainable Ensemble Trees. Computational Statistics, DOI: 10.1007/s00180-022-01312-6.
Aria, M., Cuccurullo C, D’Aniello L, Misuraca M., Spano M. (2022). Thematic Analysis as a New Culturomic Tool: The Social Media Coverage on COVID-19 Pandemic in Italy. Sustainability, 14(6), 3643, DOI: 10.3390/su14063643.
Pandolfo G., Iorio C., Staiano M., Aria M., Siciliano R. (2021). Multivariate process control charts based on the Lp depth. Applied Stochastic Models in Business and Industry, DOI: 10.1002/asmb.2616.
Aria M., Cuccurullo C., Gnasso A. (2021). A comparison among interpretative proposals for Random Forests. Machine learning with Applications, DOI: 10.1016/j.mlwa.2021.100094.
Iorio C., Aria M., D’Ambrosio A., Siciliano R. (2019). Informative Trees by Visual Pruning. Expert Systems with Applications, DOI: 10.1016/j.eswa.2019.03.018.
Aria M., D’Ambrosio A., Iorio C., Siciliano R., Cozza V. (2018). Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images. Statistical Papers, DOI: 10.1007/s00362-018-0997-x.
Siciliano R., D’Ambrosio A., Aria M., Amodio S. (2017). Analysis of Web Visit Histories, Part II: Predicting Navigation by Nested STUMP Regression Trees. Journal of Classification, DOI: 10.1007/s00357-017-9239-5.
D’Ambrosio A., Aria M., et al. (2017). Regression trees for multivalued numerical response variables. Expert Systems with Applications, 69, 1339–1351, DOI: 10.1016/j.eswa.2016.10.021.
Siciliano R., D’Ambrosio A., Aria M., Amodio S. (2016). Analysis of Web Visit Histories, Part I: Distance-Based Visualization of Sequence Rules. Journal of Classification, DOI: 10.1007/s00357-016-9204-8.
D’Ambrosio A., Aria M., et al. (2012). Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm. Journal of Classification, 29(2), 227–258, DOI: 10.1007/s00357-012-9108-1.
Aria, M. (2009). Parallel networks for compositional longitudinal data. Italian Journal of Applied Statistics, 20(1), 5–20.