Social Networks

The outbreak of Coronavirus disease, 2019 (COVID-19), started in late 2019 and developed into a pandemic by March 2020 and has become a global problem. Following the global outbreak and coronavirus spreading around the world, the WHO reported a statement on January 11, 2020, announcing the new Coronavirus outbreak as the sixth significant public health emergency in the world. In the stressful situation caused by the coronavirus epidemic, many jokes and Humor about this disease were distributed on social networks. In these circumstances, the question arises: Why do some people continue to make jokes about it, despite the mass perception of the coronavirus epidemic? The present research method was qualitative and Strauss and Corbin's version of the grounded theory was used. Participants were included the Telegram Social Network Comic Channel “https://t.me/s/jokcom” Members, which had more than 2879 members and those on Instagram and Twitter members who liked the corona content to the jokes about the covid-19 pandemic inside Iran. Based on the result, we found the effects and consequences of corona jokes. There was several factors involved in shaping the phenomenon of covids jokes. Joke and Humor are like a double-edged sword; in some situation, can be both harmful and helpful.

The main aim of this study was to identify and analyze the effect of social capital on entrepreneurial activities of Fars province specialized companies in Iran to improve their status. This descriptive research was accomplished using survey and required data was collected through questionnaire. Stratified random sampling was used and sample size was estimated to be 380 rural women. The validity and reliability of the questionnaire was confirmed by the viewpoints of professors as well as conducting a pilot study, calculating the Cronbach's alpha coefficient, respectively. Based on the results of path analysis, social capital and social networks activities had a direct and significant effect on entrepreneurial activities. Business plan writing skill, creativity, entrepreneurial motivation, family communication, supportive policies, educational-counseling policies, and business environments also had a direct and significant effect on social capital. Creativity, entrepreneurial motivation, family communication and educational policies had a direct and significant effect on social networking activities, as well. Enhancing the ability of entrepreneurs to start and continue entrepreneurial activities, paying attention to the role of social networks and their interacting as well as considering social capital as a link between business networks by identifying entrepreneurial opportunities and providing resources and facilities are essential in this regard.

A social network consists of individuals and the relationships between them, which often influence each other. This influence can propagate behaviors or ideas through the network, a phenomenon known as influence propagation. This concept is crucial in applications like advertising, marketing, and public health. The influence maximization (IM) problem aims to identify key individuals in a social network who, when influenced, can maximize the spread of a behavior or idea. Given the NP-hard nature of IM, non-exact algorithms, especially metaheuristics, are commonly used. However, traditional metaheuristics like the variable neighborhood search (VNS) struggle with large networks due to vast solution spaces. This paper introduces DQVNS (Deep Q-learning Variable Neighborhood Search), which integrates VNS with deep reinforcement learning (DRL) to enhance neighborhood structure determination in VNS. By using DQVNS, we aim to achieve performance similar to population-based algorithms and utilize the information created step by step during the algorithm's execution. This adaptive approach helps the VNS algorithm choose the most suitable neighborhood structure for each situation and find better solutions for the IM problem. Our method significantly outperforms existing metaheuristics and IM-specific algorithms. DQVNS achieves a 63% improvement over population-based algorithms on various datasets. The results of implementation on different real-world social networks of varying sizes demonstrate the superiority of this algorithm compared to existing metaheuristic, IM-specific algorithms, and network-specific measures.