Abstract
With the rapid development of online social networks, there is a trend of information interconnection among online social networks. User’s social behaviors are no longer confined to a single network but interact in the form of overlapping networks. Studying the problem of maximizing the influence of overlapping networks can not only make the influence spread to the extent that a single network cannot reach, but also effectively control the cost of advertising while maintaining the scope of diffusion. However, the existing models of user identification and influence propagation in overlapping networks are still not efficient enough. For this reason, this paper studies the problem of maximizing influence in overlapping networks based on user interests. First, network coupling is carried out through the “bridge” role of overlapping network users; secondly, independent cascade model is used. On the basis of this, a User Interest-based Influence Propagation Model of the Overlay Network (UI-IPM) is designed. Finally, based on the UI-IPM model, a heuristic algorithm combined with the greedy algorithm is designed to maximize the impact of overlapping networks (UI-IPM) and achieve Influence Maximization of the Overlay Network (IMON) mining seed nodes. The experimental results show the effectiveness of IMON algorithm in terms of influence sphere and time efficiency, and also verify the efficiency of seed nodes mining in overlapping network environment compared with a single network environment.
Highlights
The emergence of online social network (OSN) services such as Facebook, Twitter, Instagram and Google+ etc., is exerting positive impacts on data-centric applications by the way of exploiting the social elements of OSNs [1]–[4]
Aiming at the time overhead problem which increases with the network scale, a seed node selection method (IMON) combining heuristic and greedy algorithms is proposed to solve the problem of maximizing the impact of overlapping networks
2) Based on UI-IPM model, this paper studies and designs an overlapping network influence maximization algorithm (IMON) which combines a heuristic algorithm with a greedy algorithm
Summary
The emergence of online social network (OSN) services such as Facebook, Twitter, Instagram and Google+ etc., is exerting positive impacts on data-centric applications by the way of exploiting the social elements of OSNs [1]–[4]. Based on the independent cascade model, an interest-driven overlapping network influence propagation model (UI-IPM) is proposed to simulate the mode of information transmission within and between networks to make up most of users’ interest for high accuracy. Aiming at the time overhead problem which increases with the network scale, a seed node selection method (IMON) combining heuristic and greedy algorithms is proposed to solve the problem of maximizing the impact of overlapping networks.
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