Mining diversity on social media networks

Abstract

The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant's diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. We first lay out two criteria that capture the semantic meaning of diversity, and then propose a compliant definition which is simple enough to embed the idea. Based on the approach, we can measure not only a user's sociality and interest diversity but also a social media's user diversity. An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic and real social media datasets give interesting results, where individual nodes identified with high diversities are intuitive.

A media-based social interactions analysis procedure

“…We also plan to extend our analysis to consider the diversity [20] of the social connections, which demands our dataset to be restricted to those users who also gave us access to their social networks connections and preferences. In this restricted dataset, we will also be able to analyse interactions involving groups of users [3].…”

Extracting Regular Behaviors from Social Media Networks

“…To gain a deep understanding of the structures and functions of these network datasets, it is fundamental to investigate various properties of the network and its constituent components, i.e., nodes, edges, coherent subnetworks, etc [3]. For example, modeling dynamic social network [4,5], mining frequent dynamic subgraph [6], and clustering community structures in social networks [7,8].…”

“…The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. ; Liu et al [3] study diversity concept so as to characterize how diverse a given node connects with its peers in social media networks. The approach they proposed can measure not only a user's sociality and interest diversity but also a social media's user diversity.…”

“…An edge is created when someone participates in the group, contrariwise, the related edge is deleted. Let the frequent threshold be 4, a frequent subgraph constituted by edge (1,2), (1,3) and (2,3) can be extracted from the network. Furthermore, the triangle subgraph…”

“…Consequently, the edge (1,3) is created or deleted at the same intervals. These evolving behaviors imply that there are some important relations between the triangle subgraph and edge (1,3).…”