Abstract
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.

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Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient’s attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features. © Springer Nature Switzerland AG 2019.

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