Community detection and Social Network analysis based on the Italian wars of the 15th century
| Autor: | Mária Minárová, Humberto Bustince, Javier Fumanal-Idocin, Amparo Alonso-Betanzos, Oscar Cordón |
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| Přispěvatelé: | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Cities |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Networks and Communications Computer science Computer Science - Artificial Intelligence 02 engineering and technology 0202 electrical engineering electronic engineering information engineering Cluster analysis Set (psychology) Social network analysis Social and Information Networks (cs.SI) Social network Community detection business.industry Multi-agent systems Social network analysis (criminology) 020206 networking & telecommunications Computer Science - Social and Information Networks Data science Human social behaviour Artificial Intelligence (cs.AI) Hardware and Architecture Scale (social sciences) 020201 artificial intelligence & image processing business Simulation Software |
| Zdroj: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra instname |
| Popis: | 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. Corrections in: Revamped affinity section, conclusions and minor changes in the introduction. Also, the dynamic delta section is expanded a bit |
| Databáze: | OpenAIRE |
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