Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.

At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, especially the Twitter platform, is experiencing a high volume of activity related to this pandemic. Among the hot topics, the polarized debates about unconfirmed medicines for the treatment and prevention of the disease have attracted significant attention from online media users. In this work, we present a stance data set, COVID-CQ, of user-generated content on Twitter in the context of COVID-19. We investigated more than 14 thousand tweets and manually annotated the tweet initiators’ opinions regarding the use of “chloroquine” and “hydroxychloroquine” for the treatment or prevention of COVID-19. To the best of our knowledge, COVID-CQ is the first data set of Twitter users’ stances in the context of the COVID-19 pandemic, and the largest Twitter data set on users’ stances towards a claim, in any domain. We have made this data set available to the research community via the Mendeley Data repository. We expect this data set to be useful for many research purposes, including stance detection, evolution and dynamics of opinions regarding this outbreak, and changes in opinions in response to the exogenous shocks such as policy decisions and events.

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Because of the critical role of emotional and unconscious processes in consumers' decision making, understanding the human brain and neural performance scope varied to better understanding the behavior and predict the consumers' decisions. In this regard, the combination of interdisciplinary studies in the fields of cognitive science has led to the creation of neuromarketing. Neuromarketing is a field of marketing research that studies consumers' sensorimotor, cognitive, and affective response to marketing stimuli. Objective: The aim of this study was to investigate the role of N1 component of Event-Related Potential (ERP) in measuring consumers' preferences in the face of the brand beverages. Methods: 26 subjects in the age range of 18 to 26 years old (13 males, mean age = 24.40, SD = 1.34 and 13 females, mean age = 22.60, SD = 2.87) were examined, for equalizing of subjects' context and increasing their attention, a short story was told to choose a drink. The designed task was displayed and simultaneously the event-related potentials (ERPs) were recorded. The results of the ERP data were evaluated by statistical analysis of repeated measurement ANOVA to check individual brand preferences to the products, in 2 categories (familiar-unfamiliar). Results: A large significant difference has been seen in N1 component amplitude in occipital lobe for familiar logos than unfamiliar ones which refers to a pre-comprehension brain activity. Conclusion: changes for N1 in occipital lobes showed significant larger amplitude especially in right site of brain that it can be considered as an effective factor in predicting the preference of consumers.

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