BUSCADOR RECOLECTA

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Fumanal-Idocin, J., Alonso-Betanzos, A., Cordón, O., Bustince, H., & Minárová, M. (2020). ‘Community detection and social network analysis based on the Italian wars of the 15th century’ has been accepted for publication in: Future Generation Computer Systems, 113, 25–40. The Version of Record is available online at https://doi.org/10.1016/j.future.2020.06.030, [Abstract]: In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network. By using these functions, we develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction in the network. We also discuss the effects of size and scale for communities regarding this case, as well as how we cope with the additional complexity present when big communities arise. Finally, we compare our community detection solution with other representative algorithms, finding favourable results., Javier Fumanal Idocin’s and Humberto Bustince’s research has been supported by the project TIN2016-77356-P (AEI/FEDER,UE). Oscar Cordón’s research was supported by the Spanish Ministry of Science, Innovation and Universities under grant EX-ASOCO (PGC2018-101216-B-I00), including, European Regional Development Funds (ERDF). Amparo Alonso-Betanzos’ research has been financially supported in part by the Spanish Ministerio de Economía y Competitividad (research project TIN2015-65069-C2-1-R), by European Union FEDER funds and by the Consellería de Industria of the Xunta de Galicia, Spain (research project GRC2014 /035). M. Minárová’s research has been funded by the project work was supported by the project APVV-17-0066., Xunta de Galicia; GRC2014 /035, Slovak Republic. Slovak Research and Development Agency; APVV-17-0066

Age estimation is a fundamental task in forensic anthropology for both the living and the dead. The procedure consists of analyzing properties such as appearance, ossification patterns, and morphology in different skeletonized remains. The pubic symphysis is extensively used to assess adults’ age-at-death due to its reliability. Nevertheless, most methods currently used for skeleton-based age estimation are carried out manually, even though their automation has the potential to lead to a considerable improvement in terms of economic resources, effectiveness, and execution time. In particular, explainable machine learning emerges as a promising means of addressing this challenge by engaging forensic experts to refine and audit the extracted knowledge and discover unknown patterns hidden in the complex and uncertain available data. In this contribution we address the automation of the decision making process of Todd’s pioneering age assessment method to assist the forensic practitioner in its application. To do so, we make use of the pubic bone data base available at the Physical Anthropology lab of the University of Granada. The machine learning task is significantly complex as it becomes an imbalanced ordinal classification problem with a small sample size and a high dimension. We tackle it with the combination of an ordinal classification method and oversampling techniques through an extensive experimental setup. Two forensic anthropologists refine and validate the derived rule base according to their own expertise and the knowledge available in the area. The resulting automatic system, finally composed of 34 interpretable rules, outperforms the state-of-the-art accuracy. In addition, and more importantly, it allows the forensic experts to uncover novel and interesting insights about how Todd’s method works, in particular, and the guidelines to estimate age-at-death from pubic symphysis characteristics, generally.

En este trabajo presentamos el modelado de redes
sociales y detección de comunidades utilizando como base un evento histórico real, las conquistas de César Borgia en el siglo XV. Para ello, proponemos un nuevo conjunto de funciones, llamadas funciones de afinidad, disenadas para capturar la 'naturaleza de las interacciones locales entre cada par de actores
en una red. Utilizando estas funciones, desarrollamos un nuevo algoritmo de detección de comunidades, el Borgia Clustering, donde las comunidades surgen naturalmente de un proceso de simulación de interacción de múltiples agentes en la red. También discutimos los efectos del tamaño y la escala de cada
comunidad, y como pueden ser tomadas en cuenta en el proceso de simulación. Finalmente, comparamos nuestra detección de comunidades con otros algoritmos representativos, encontrando
resultados favorables a nuestra propuesta., El trabajo de Javier Fumanal Idocin y Humberto Bustince
ha sido financiado por el proyecto PID2019-108392GB-I00
(AEI/10.13039/ 501100011033).
El trabajo de Oscar Cordón ha sido financiado por el
Gobierno de España, EXASOCO (PGC2018-101216-B-I00),
incluyendo fondos de desarrollo regional europeo (ERDF). La investigación de Amparo Alonso Betanzos ha sido
parcialmente financiado por el Ministerio de Economía y
Competitividad de España (TIN2015-65069-C2-1-R), por los
fondos europeos FEDER y por la Consellería de Industria de
la Xunta de Galicia (GRC2014 /035).
El trabajo de María Minárová ha sido financiado por los
proyectos APVV-17-0066, and APVV-18-0052.

Social network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network. One of the most popular problems in social network analysis is finding communities in its network structure. Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition of community is somewhat imprecise, many algorithms have been proposed to solve this task, each of them focusing on different social characteristics of the actors and the communities. In this work we propose to use novel combinations of affinity functions, which are
designed to capture different social mechanics in the network interactions. We use them to extend already existing community detection algorithms in order to
combine the capacity of the affinity functions to model different social interactions than those exploited by the original algorithms., Javier Fumanal Idocin and Humberto
Bustince’s re-search has been supported
by the project PID2019-108392GBI00
(AEI/10.13039/501100011033).
Maria Minarová research has been funded by the project work was supported by the projects APVV-17-0066 andAPVV-18-0052.
Oscar Cordon’s research was supported by the Spanish Ministry of Science, Innovation and Universities under grant EXASOCO (PGC2018-101216-B-I00), including, European Regional Development Funds (ERDF).