A diffusion mechanism over interest-based communities in Mobile Internet

Mobile Internet has played an essential role in our daily life. An overwhelming majority of marketers have noticed the great opportunity to diffuse information by this platform. However, most of the diffusion mechanisms are for a certain mobile social media, which can not reflect the characteristics of innovation diffusion in whole mobile Internet. In this paper, we use a three-step method to identify the hidden relations among different users, and establish a number of interest-based communities based on the characteristics of mobile Internet. Considering the structure of these communities and user preferences, we propose a novel diffusion mechanism. Our experimental results show that the proposed model can provide an ideal effectiveness on innovation diffusion.

[1]  Mingquan Wu,et al.  On Accelerating Content Delivery in Mobile Networks , 2013, IEEE Communications Surveys & Tutorials.

[2]  Bin Wu,et al.  Community detection in large-scale social networks , 2007, WebKDD/SNA-KDD '07.

[3]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[4]  Robin I. M. Dunbar.  Coevolution of neocortical size, group size and language in humans , 1993, Behavioral and Brain Sciences.

[5]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[6]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[7]  Lori Rosenkopf,et al.  Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation , 1997 .

[8]  Yung-Ming Li,et al.  A diffusion mechanism for social advertising over microblogs , 2012, Decis. Support Syst..

[9]  Mark H. Chignell,et al.  Automatic detection of cohesive subgroups within social hypertext: A heuristic approach , 2008, New Rev. Hypermedia Multim..

[10]  Tammo H. A. Bijmolt,et al.  Will It Spread or Not? The Effects of Social Influences and Network Topology on Innovation Diffusion , 2010 .

[11]  Mark E. J. Newman,et al.  An efficient and principled method for detecting communities in networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  S. Horvath,et al.  Statistical Applications in Genetics and Molecular Biology , 2011 .

[13]  Rossano Schifanella,et al.  Link creation and information spreading over social and communication ties in an interest-based online social network , 2012, EPJ Data Science.

[14]  Przemyslaw Kazienko,et al.  Group Evolution Discovery in Social Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[15]  Nirwan Ansari,et al.  Identifying website communities in mobile internet based on affinity measurement , 2014, Comput. Commun..

[16]  Kevin Lewis,et al.  Social selection and peer influence in an online social network , 2011, Proceedings of the National Academy of Sciences.

[17]  Alvin Chin.  Finding Cohesive Subgroups and Relevant Members in the Nokia Friend View Mobile Social Network , 2009, 2009 International Conference on Computational Science and Engineering.