As social media use has grown, it has become, like any other social activity, a phenomenon amenable to scientific study. One approach applies the already well-established tools of network analysis and builds on them further to address the specific questions that arise in the study of social networks.
Our journal, Computational Social Networks focuses specifically on the social computing aspect of this. The journal looks at the theoretical foundation, mathematical aspects, and applications of social computing. You can see this in the range of recent articles—and thematic series—the journal publishes. For example:
- Using attractiveness model for actors ranking in social media networks, by Ziyaad Qasem, Marc Jansen, Tobias Hecking, and H. Ulrich Hoppe.
- Influential actors detection in social media such as Twitter or Facebook can play a major role in gathering opinions on particular topics, improving the marketing efficiency, predicting the trends, etc
- Modelling and analysis of the dynamics of adaptive temporal–causal network models for evolving social interactions, by Jan Treur
- Network-Oriented Modelling based on adaptive temporal–causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes.
- Effect of direct reciprocity and network structure on continuing prosperity of social networking services, by Kengo Osaka, Fujio Toriumi, and Toshihauru Sugawara
Social networking services (SNSs) are widely used as communicative tools for a variety of purposes. SNSs rely on the users’ individual activities associated with some cost and effort, and thus it is not known why users voluntarily continue to participate in SNSs. Because the structures of SNSs are similar to that of the public goods (PG) game, some studies have focused on why voluntary activities emerge as an optimal strategy by modifying the PG game. However, their models do not include direct reciprocity between users, even though reciprocity is a key mechanism that evolves and sustains cooperation in human society.
- Real-time topic-aware influence maximization using preprocessing, by Wei Chen, Tian Lin, and Cheng Yang
- nfluence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics
And the thematic series, guest-edited by Hocine Cherfi: Complex Networks.
We’re proud of what the Computational Social Networks has already accomplished in its first three years, and excited for the future. We invite you to take a look at the journal for yourself.
