Networks in the Global World V: Proceedings of NetGloW 2020. Lecture Notes in Networks and Systems
Signed networks form a particular class of complex networks that has many applications in sociology, recommender and voting systems. The contribution of this paper is twofold. First, we propose an approach aimed at determining the characteristic subgraphs of the network. Second, we apply the developed approach to the analysis of the network describing the Wikipedia adminship elections. It is shown that this network agrees with the status theory if one does not consider strongly tied vertices, i.e., the vertices that are connected in both directions. At the same time, the strongly connected vertices mostly agree with the structural balance theory. This result indicates that there is a substantial difference between single and double connections, the fact that deserves a detailed analysis within a broader context of directed signed networks.
Online social networks have become an essential communi- cation channel for the broad and rapid sharing of information. Currently, the mechanics of such information-sharing is captured by the notion of cascades, which are tree-like networks comprised of (re)sharing actions. However, it is still unclear what factors drive cascade growth. Moreover, there is a lack of studies outside Western countries and platforms such as Facebook and Twitter. In this work, we aim to investigate what fac- tors contribute to the scope of information cascading and how to predict this variation accurately. We examine six machine learning algorithms for their predictive and interpretative capabilities concerning cascades’ structural metrics (width, mass, and depth). To do so, we use data from a leading Russian-language online social network VKontakte capturing cascades of 4,424 messages posted by 14 news outlets during a year. The results show that the best models in terms of predictive power are Gradient Boosting algorithm for width and depth, and Lasso Regression algorithm for the mass of a cascade, while depth is the least predictable. We find that the most potent factor associated with cascade size is the number of reposts on its origin level. We examine its role along with other factors such as content features and characteristics of sources and their audiences.
Co-playing, or playing video games together, is a social practice that enriches relationships and game experience by providing the players with informational and social support. This study explores how co-playing integrates into friendship in two small (6–7 people), male communities of adolescent and adult friends. Both communities are local and school-based; both focus on co-playing Dota 2. The study focuses on the leadership in these small networks, compares their co-playing patterns, and the ways in which co-playing affects the relationships in both communities, enhancing their bonding social capital. We apply network analysis and personal interviews to compare and contrast how the co-playing communities emerged, are maintained and evolve along with the friendship. The main conclusion is that such co-playing communities emerge around a single Dota 2 enthusiast in the early secondary school as a common pastime, but co-playing video games increases bonding social capital among the community members. Network analysis demonstrates the differences in leadership in the teen and adult communities. The research shows how video games are embedded in collective everyday friendship and how co-playing communities function in support of such a relationship. The findings could be further tested against female and mixed co-playing communities.
Rich data from social network sites (SNS) attracts the attention of psychologists and sociologists interested in interpersonal dynamics, friendship networks, and social capital. The presented study explores the effect of network structural features and psychological characteristics of SNS users on changes in their friendship networks. The data from the representative and diverse sample of 375 Russian Vkontakte SNS users from Vologda city was used. Two waves of network data collection allow us to estimate changes in the size of the friendship networks. Regression analysis reveals similarities in the factors responsible for the changes in networks for users who attract or reject friends. We discuss possible explanations of this phenomenon, as well as limitations of the study and further research directions.