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Graduate in Applied Physics/Mathematics (Astronomy) and master in Computational Methods in Sciences and Engineering.

I was connected to the dissemination of Astronomy until 2004, in the Planetariums of Porto and Espinho.

In 2005, I started working as a researcher at INEGI, where I stayed until 2011.

From then until 2016, I had and took care of my son.

I have been part of the INESC-TEC family since October 2016, developing my work in Network Science, more precisely in multilayer networks.

In 2018, I started the PhD Program in Engineering and Public Policy.



  • Name

    Patrícia Teixeira Gonçalves
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    01st October 2016


Centrality and community detection: a co-marketing multilayer network

Fernandes, A; Goncalves, PCT; Campos, P; Delgado, C;

Journal of Business and Industrial Marketing

Purpose: Based on the data obtained from a questionnaire of 595 people, the authors explore the relative importance of consumers, checking whether socioeconomic variables influence their centrality, detecting the communities within the network to which they belong, identifying consumption patterns and checking whether there is any relationship between co-marketing and consumer choices. Design/methodology/approach: A multilayer network is created from data collected through a consumer survey to identify customers’ choices in seven different markets. The authors focus the analysis on a smaller kinship and cohabitation network and apply the LART network community detection algorithm. To verify the association between consumers’ centrality and variables related to their respective socioeconomic profile, the authors develop an econometric model to measure their impact on consumer’s degree centrality. Findings: Based on 595 responses analysing individual consumers, the authors find out which consumers invest and which variables influence consumers’ centrality. Using a smaller sample of 70 consumers for whom they know kinship and cohabitation relationships, the authors detect communities with the same consumption patterns and verify that this may be an adequate way to establish co-marketing strategies. Originality/value: Network analysis has become a widely used technique in the extraction of knowledge on consumers. This paper’s main (and novel) contribution lies in providing a greater understanding on how multilayer networks represent hidden databases with potential knowledge to be considered in business decisions. Centrality and community detection are crucial measures in network science which enable customers with the highest potential value to be identified in a network. Customers are increasingly seen as multidimensional, considering their preferences in various markets. © 2019, Emerald Publishing Limited.


Mr. Silva and patient zero: A medical social network and data visualization information system

Goncalves, PCT; Moura, AS; Cordeiro, MNDS; Campos, P;

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Detection of Patient Zero is an increasing concern in a world where fast international transports makes pandemia a Public Health issue and a social fear, in cases such as Ebola or H5N1. The development of a medical social network and data visualization information system, which would work as an interface between the patient medical data and geographical and/or social connections, could be an interesting solution, as it would allow to quickly evaluate not only individuals at risk but also the prospective geographical areas for imminent contagion. In this work we propose an ideal model, and contrast it with the status quo of present medical social networks, within the context of medical data visualization. From recent publications, it is clear that our model converges with the identified aspects of prospective medical networks, though data protection is a key concern and implementation would have to seriously consider it. © Springer Nature Switzerland AG 2018.