Exploiting Online Social Network Structural Properties for Information Spreading
Keywords:
Network structural properties, influence radius, information dissemination, online social networks.Abstract
The ability to influence individuals on online social
networks for dissemination of information is crucial for
commercial advertising, online marketing, political
campaigning and, for the general public. However, there is still a research gap in understanding the underlying structure of these networks, their structural properties and how these properties can be leveraged in other research areas. Though information dissemination is a key objective of most online social networks, several influence models that are proposed in the literature are based on simulations, greedy and heuristic approaches, which sometimes are computationally expensive.
Thus, these approaches do not take advantage of the underlying properties of these networks for effective and efficient information dissemination. This is because these network structural properties are not well-studied couples with the computationally expensive algorithms for implementing information diffusion on them. To this end, we propose to address these gaps in three folds. Firstly, the structural properties of several online social networks are studied to have a thorough overview of their underlying structure. Secondly, an efficient information diffusion algorithm is proposed and
implemented with a less computational time that scales Graph theory tool is used to model these relationships and then further analyze to decode the intricate underlying network properties behind these systems.
Presently, online social networks are the portal through
which information is disseminated to individuals. User traffic on these social platforms now rivals that of the traditional web. These networks play an important role in the spread of information and influence and, therefore, are of interest to business analysts, politicians and even the human society as a whole. For instance, a huge amount of money is spent yearly on digital ad advertisement on online social networks. It was
estimated that worldwide social network ad spending will reach billion in 2014, an increase of from
2013 and will push social networks share of the overall
digital ad investment to [1], and will collectively ,
where N is the number of nodes. Thirdly, we apply the
algorithm to these networks and an influence index is calculated on them in order to study the impact of their structural properties on information diffusion and also as a way to characterize them. The results show that the networks structural properties of online social networks such as the average clustering coefficient, average degree, degree entropy, edge entropy among others, are effective in disseminating information as they correlate well with the influence index.
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