Details
Name
Pedro CamposCluster
Computer ScienceRole
Senior ResearcherSince
01st January 2010
Nationality
PortugalCentre
Artificial Intelligence and Decision SupportContacts
+351220402963
pedro.campos@inesctec.pt
2021
Authors
Gonçalves, PCT; Moura, AS; Cordeiro, MNDS; Campos, P;
Publication
Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management
Abstract
2021
Authors
Pratesi M.; Campos P.;
Publication
Statistical Journal of the IAOS
Abstract
After 12 years of EMOS experience it is time to open the discussion on the future of EMOS. This papers briefly describes the experience from the perspective of the Universities, trying also to describe the needs and role of the NSIs, Banks and other possible actors to join the network, and unlock the future. EMOS should reload (or evolute) to stay current and attractive. Statistical 'thinking' evolved and a major change and challenge for EMOS is to pick up this trend in its cooperation with the universities.
2020
Authors
Valka, K; Roseira, C; Campos, P;
Publication
Industry and Higher Education
Abstract
2020
Authors
Duarte, P; Campos, P;
Publication
Advances in Intelligent Systems and Computing - Decision Economics: Complexity of Decisions and Decisions for Complexity
Abstract
2020
Authors
Vieira, AR; Campos, P; Brito, P;
Publication
JOURNAL OF COMPLEX NETWORKS
Abstract
Community detection techniques use only the information about the network topology to find communities in networks Similarly, classic clustering techniques for vector data consider only the information about the values of the attributes describing the objects to find clusters. In real-world networks, however, in addition to the information about the network topology, usually there is information about the attributes describing the vertices that can also be used to find communities. Using both the information about the network topology and about the attributes describing the vertices can improve the algorithms' results. Therefore, authors started investigating methods for community detection in attributed networks. In the past years, several methods were proposed to uncover this task, partitioning a graph into sub-graphs of vertices that are densely connected and similar in terms of their descriptions. This article focuses on the analysis and comparison of some of the proposed methods for community detection in attributed networks. For that purpose, several applications to both synthetic and real networks are conducted. Experiments are performed on both weighted and unweighted graphs. The objective is to establish which methods perform generally better according to the validation measures and to investigate their sensitivity to changes in the networks' structure and homogeneity.
Supervised Thesis
2021
Author
Vanessa Correia Pinto
Institution
UP-FEP
2021
Author
Kerley de Lourdes Silva Pires
Institution
UP-FEP
2021
Author
Ricardo André Fernandes da Silva
Institution
UP-FEP
2021
Author
Daniel Carvalho Marques
Institution
UP-FEP
2021
Author
Bárbara Monteiro Santos
Institution
UP-FEP
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