Abstract
With the increasing popularity and rapid development of Online Social Networks (OSNs), OSNs not only bring fundamental changes to information and communication technologies, but also make an extensive and profound impact on all aspects of our social life. Efficient content discovery is a fundamental challenge for large-scale distributed OSNs. However, the similarity between social networks and online social networks leads us to believe that the existing social theories are useful for improving the performance of social content discovery in online social networks. In this article, we propose an interest-aware social-like peer-to-peer (IASLP) model for social content discovery in OSNs by mimicking ten different social theories and strategies. In the IASLP network, network nodes with similar interests can meet, help each other, and co-operate autonomously to identify useful contents. The presented model has been evaluated and simulated in a dynamic environment with an evolving network. The experimental results show that the recall of IASLP is 20% higher than the existing method SESD while the overhead is 10% lower. The IASLP can generate higher flexibility and adaptability and achieve better performance than the existing methods.
Highlights
Online Social Networks (OSNs) such as Facebook, Twitter, LinkedIn, Google+, etc. have become popular Internet platforms where people around the world can share their social contents
As with people in social networks, each peer node in an interest-aware social-like peer-to-peer (IASLP) network shares an interest attribute associated with its own social contents to help form content communities based around common interests
People can search for social contents by directly contacting friends or acquaintances that may potentially possess, or have knowledge about, the desired social contents
Summary
Online Social Networks (OSNs) such as Facebook, Twitter, LinkedIn, Google+, etc. have become popular Internet platforms where people around the world can share their social contents. Some studies [Liao et al 2010; Hu et al 2014] assign peer nodes to different groups or communities according to the interests of users These studies exploit similarity of interests of users in the same group, theories of small-world [Watts and Strogatz 1998; Kleinberg 2000] and scale-free [Barabási and Albert 1999] features to optimize the search algorithms for P2P networks and improve performance of content discovery. Self-organized method in studies of Liu et al [2009; 2016] has a lower overhead, it neither considers forming content communities according to contents of peer nodes with similar interests nor exploits the number of contents of peer nodes to improve search efficiency.
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