Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algorithms have been implemented to unfold the structures of communities, the influence of NMI on the Q, and vice versa, between a detected partition and the correct partition in signed and unsigned networks is unclear. For this reason, in this paper, we investigate the correlation between Q and NMI in signed and unsigned networks. The results show that there is no direct relationship between Q and NMI in both types of networks.
Background: Coronavirus pandemic (COVID-19) has enormously affected various healthcare services including the one of community pharmacy. The ramifications of these effects on Iraqi community pharmacies and the measures they have taken to tackle the spread of COVID-19 is yet to be explored. In this cross sectional survey, infection control measures by community pharmacies in Sulaimani city/Iraq has been investigated.
Methods: Community pharmacists were randomly allocated to participate in a cross-sectional survey via visiting their pharmacies and filling up the questionnaire form.
Results and discussion:
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