International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The new platforms coming out on the social media space are faced from the beginning with a fierce contest. The competition for user attention is hard and the main players (Facebook, TikTok, Instagram, Reddit) have a huge advantage. To overcome these problems many of the newly lunched platform are making use of gamification strategy to keep the user engaged. The gamification strategy usually provides the user with a form of digital reputation but on some platform, it can be even a form of digital currency which can be exchanged for benefits or real products. To validate the user actions there is not always an automatic process, and the platforms are relaying on the community to cross-validate the authenticity and the value of the respective actions. The validation of the user actions can be automated but not always. When the validation cannot be automated, and the task requires human intervention the process is in his most part outsourced to the community and only a small number of the validation will be done by platform operators. The validation done by the platform operators will give credits to the users whose actions were validated. After certain amount of credit is given to a user, he became trusted, and his action can validate the actions of other users. The trusted user will act almost as a platform operator by validating other users’ actions. In this paper we simulate and analyse how the trust can be propagated into different social networks configuration. We start by defining a mathematical framework for modelling the users and the actions. After we introduce two different algorithms for propagating the trust in the community and finally for each configuration, we simulate the propagation of the trust and discuss the results.

More than one century network science (NS) has been extensively used in numerous studies of different research fields, including public transport (PT). This work presents an application of a NS tool enabling to explore complex public-transport networks (PTNs). This tool explores networks for small-world, scale-free and random network characteristics, in a case study to examine and analyze, by using NS concepts, two PT systems – Washington DC’s Metro Network (WMN) and Oslo’s Metro Network (OMN). The performed analyses focused on the structure and the topological properties of the examined networks. As the networks have longer average path
lengths, compared with random networks, and because of not being clustered, these metro networks demonstrated they are not small-world networks. The analyses also show that in contrast to the OMN the WMN has certain characteristics associated with scale-free networks; that is, a small number of highly connected nodes (hubs) and node degree distribution that can be represented by a power-law function. Nevertheless, it still cannot be considered as a pure scale-free network because of its empirical distribution which is better approximated by an exponential rather than a power-law function. The metro WMN cannot be considered a random network because of having hubs. Thus, it is concluded that the examined WMN is an evolving complex network.