Abstract
The main goal of this article is to investigate techniques that can quickly lead to successful social systems by boosting network connectivity. This is especially useful when starting new online communities where the aim is to increase the system utilization as much as possible. This aspect is very important nowadays, given the existence of many online social networks available on the web, and the relatively high level of competition. In other words, attracting users’ attention is becoming a major concern, and time is an essential factor when investing money and resources into online social systems. Our study describes an effective technique that deals with this issue by introducing the notion of mermaids, special attractors that alter the normal evolutive behavior of a social system. We analyze how mermaids can boost social networks, and then provide estimations of fundamental parameters that business strategists can take into account in order to obtain successful systems within a constrained budget.
Highlights
Social networks are nowadays among the most important and successful online systems
We analyze online social networks viewing them as complex systems: Whereas in a previous paper we studied complex systems along their spatial informational axes, this paper instead tackles the other informative axis, time
We study the use of this novel tool by considering its use within practical scenarios: Using a financial technology setting, we look for the best business strategies that can be used when operating within a certain set budget, predicting the best configurations in term of number of mermaids, attractiveness, and length of time these nodes are effective
Summary
Social networks are nowadays among the most important and successful online systems. Since 2010, when Facebook became the most visited site in the US beating all other players including Google, we have witnessed an enormous growth of the social world, with many social systems arising, and with many other online systems adding social functionalities. This huge growth has correspondingly brought lot of competition, making it very hard to build new successful social networks. We think that the models are general enough that they can fit other complex networks as well
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