what-when-how
In Depth Tutorials and Information
3.2.2.3 Predicting the Posting Behavior Based on a Machine-
Learning Approach ...........................................................52
3.2.2.4 Modeling the Posting Behavior Based on the Cascade
Model ...............................................................................53
3.2.2.5 Analysis of Users' Interests................................................56
3.3 Models and Analysis of Information Flow..................................................58
3.3.1 Discovering and Analyzing the Information Flow Pathway ............58
3.3.2 Models of Innovation Diffusion
3.3.2.1 Out-State Rate Estimation................................................62
3.3.2.2 Transition Probability .......................................................63
3.3.2.3 Recommendation Algorithm ............................................63
3.3.2.4 Ranking Algorithm ......................................................... 64
References ...........................................................................................................65
As one of the most important media, Internet provides various communication
platforms for information propagation. Especially on a blog site, BBS (Bulletin
Board System), and a forum, users can post and spread their ideas and thoughts
through an online social network, which composes of groups of users with particu-
lar patterns of communication between them [1]. Analyzing and predicting the
dynamic characteristics of information propagation is obviously helpful in design-
ing propaganda strategy and testing the performance of an advertisement, min-
ing some latent business opportunities for corporations, and recommending the
content of growing hot topics to improve users' experiences for obtaining popular
information for Social Networking Sites (SNS), etc.
his chapter is designed to introduce the related work on dynamic models and
analyze results for information propagation in online social networks. First we give
the description of the traditional epidemic model and rumor model and introduce
recent works on modeling and predicting briefly the propagation scale of informa-
tion based on them. Second, we introduce the analysis of relationship among users
and models of user behavior, such as reading and posting behavior. Finally, analysis
of information low pathway and models of innovation diffusion are introduced.
3.1 ModelsofInformationPropagationBasedon
theEpidemicModelandtheRumorModel
he traditional epidemic model is based on compartmental models, in which
individuals in the population are divided into a set of different groups. Related
theory has been used in planning, implementing, and evaluating various preven-
tion, therapy, and control programs. Based on the analogy between the spread
of disease and the spread of information in networks, the rumor model has been
investigated to describe the dynamics of the rumor propagation process. However,
