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As part of an applied activity of a university course in Japan, a total of 65 students posted messages on Twitter regarding world issues to expand the scope of scholarly communication with social users on Twitter. The students' motivation to learn was evaluated using a standard questionnaire. Gradually, social media analysis was introduced for examination from a holistic perspective. For the first time, we also attempted to use exponential random graph models to investigate the structures of a network with statistical evidences. The connections exhibited by students were classified into three types: crossing, private, and isolation. The multiple comparison test showed a significant result: students of the crossing type were more motivated than those of the isolation type. The result also showed how students communicate with users from other networks, elucidated the connections and structures of tweets, and extracted influential students from the network. In conclusion, the skills to connect with social Twitter users as well as communication among students were found to be indispensable factors in expanding the scholarly network and developing social capital in the community. [ABSTRACT FROM AUTHOR]

Simple Summary: In the early stages of the COVID-19 pandemic, Japan conducted contact tracing extensively and published detailed records of thousands of anonymized patients. We leveraged the registry data to perform an exponential random graph model (ERGM) network analysis to examine demographic and symptomological homophilies of SARS-CoV-2 transmission networks in Hokkaido and Kanagawa. Our analysis showed: (1) Age, symptom, and asymptomatic status homophilies in both prefectures; (2) Asymptomatic infections increased as the virus was passed from primary cases to secondary and tertiary ones; (3) Transmission was mostly seen at the primary and secondary levels, with none occurring beyond quaternary; (4) Transmission occurred primarily in healthcare settings, as well as in families. Kanagawa and Hokkaido were affected by COVID-19 in the early stage of the pandemic. Japan's initial response included contact tracing and PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. In this retrospective study, we analyzed publicly available COVID-19 registry data from Kanagawa and Hokkaido (n = 4392). Exponential random graph model (ERGM) network analysis was performed to examine demographic and symptomological homophilies. Age, symptomatic, and asymptomatic status homophilies were seen in both prefectures. Symptom homophilies suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Environmental variables such as temperature and humidity may also play a role in the overall pathogenesis of the virus. A higher level of asymptomatic transmission was observed in Kanagawa. Moreover, patients who contracted the virus through secondary or tertiary contacts were shown to be asymptomatic more frequently than those who contracted it from primary cases. Additionally, most of the transmissions stopped at the primary and secondary levels. As expected, significant viral transmission was seen in healthcare settings. [ABSTRACT FROM AUTHOR]

The so-called Maximum Clique Problem is one of the most famous NP-complete problems for which it is difficult to find a solution. Given an indirected graph, we present here a polynomial-time randomized algorithm RaCLIQUE for finding a near-maximum clique. While the basic idea of the algorithm comes from Boltzmann machines, it employs no simulated annealing at all and hence it is simple to control its execution. We have confirmed in experiments for several random and nonrandom graphs with up to 400 nodes that very good solutions can be found efficiently compared with the other conventional algorithms. [ABSTRACT FROM AUTHOR]