Overlapping Social Network Communities and Viral Marketing

Abstract

1 Social networks have highly been used to understand the behavior and activities of individuals in nature and society. They are being used as a means to communicate, diffuse information, and to control the spread of diseases and computer viruses, in addition to many other tasks. Business organizations look upon social networks as an opportunity to spread the word-of-mouth for viral marketing and this task has gained significance with the popularity of Online Social Networks (OSNs). However, an important characteristic of social networks, including OSNs, which is the existence of overlapping communities of users, has not been exploited yet for the task of viral marketing even though it seems promising. This paper aims to present the importance of identifying overlapping communities for the task of viral marketing in social networks and also provides some experimental results on an email network to back the claims.

Toward Information Diffusion Model for Viral Marketing in Business

“…If the probability lay between 1% and 5%, then it became a top influencer node. This supported the hypothesis proposed by Bhat and Abulaish [12]: the more a community overlaps, the greater the individual's influence in the whole network. In addition, the outlier nodes were considered noisy nodes, which proved to be non-influencing nodes when the probability reached 60%.…”

“…The viral marketing phenomenon is described as influencespreading over social networks [11]. The process of viral marketing, influence marketing, or WOM is divided into three stages: 1) initiating the advertising message , 2) locating the best seeding nodes, and 3) diffusing the marketing message to others [12].…”

“…Bhat and Abulaish [12] introduced a community detection approach to viral marketing. Their work concentrated on identifying overlapping communities in online social networks by examining the weighted online social network of email communications from Enron [57].…”

“…In addition, the outlier nodes were considered noisy nodes, which proved to be non-influencing nodes when the probability reached 60%. Bhat and Abulaish [12] attributed this to the lack of applying the concept of community detection and its attributes to viral marketing. Meng, Zhang, Zhu, Xing, Wang, and Shi [58] have proposed an incremental density-based link clustering algorithm for community detection in dynamic networks called iDBLINK.…”

Analysis and mining of online social networks: emerging trends and challenges

“…They also suggest that demographic data are useful to identify strong influencers and together with global network variables are also useful to identify adopters as well. Along a similar direction, Bhat and Abulaish analyze the influential significance of overlapping nodes, i.e., nodes that belong to multiple communities in a social network. Their analysis highlights that highly overlapping nodes in a social network represent the best influential nodes in the network in terms of their betweenness centrality and that outliers and single membership nodes can be easily discarded as least influential.…”