Model Checking Social Network Models

نویسندگان

  • Raúl Pardo
  • Gerardo Schneider
چکیده

A social network service is a platform to build social relations among people sharing similar interests and activities. The underlying structure of a social networks service is the social graph, where nodes represent users and the arcs represent the users’ social links and other kind of connections. One important concern in social networks is privacy: what others are (not) allowed to know about us. The “logic of knowledge” (epistemic logic) is thus a good formalism to define, and reason about, privacy policies. In this paper we consider the problem of verifying knowledge properties over social network models (SNMs), that is social graphs enriched with knowledge bases containing the information that the users know. More concretely, our contributions are: i) We prove that the model checking problem for epistemic properties over SNMs is decidable; ii) We prove that a number of properties of knowledge that are sound w.r.t. Kripke models are also sound w.r.t. SNMs; iii) We give a satisfaction-preserving encoding of SNMs into canonical Kripke models, and we also characterise which Kripke models may be translated into SNMs; iv) We show that, for SNMs, the model checking problem is cheaper than the one based on standard Kripke models. Finally, we have developed a proof-of-concept implementation of the model-checking algorithm for SNMs.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

DynaDiffuse: A Dynamic Diffusion Model for Continuous Time Constrained Influence Maximization

Studying the spread of phenomena in social networks is critical but still not fully solved. Existing influence maximization models assume a static network, disregarding its evolution over time. We introduce the continuous time constrained influence maximization problem for dynamic diffusion networks, based on a novel diffusion model called DYNADIFFUSE. Although the problem is NP-hard, the influ...

متن کامل

"How Did They Know?" - Model-Checking for Analysis of Information Leakage in Social Networks

We examine the use of model-checking in the analysis of information leakage in social networks. We take previous work on the formal analysis of digital crowds and show how a variation on the formalism can naturally model the interaction of people and groups of followers in intersecting social networks. We then show how probabilistic models of the forwarding and reposting behaviour of individual...

متن کامل

Using emotional intelligence to predict job stress: Artificial neural network and regression models

Introduction: These days, there is a consensus that emotional intelligence plays an important role in the success of individuals in different areas of life. Persons with higher emotional intelligence had lower stress in dealing with demands and pressures in the workplace. The purpose of this study was to use artificial neural network to predict job stress and to compare the performance of this ...

متن کامل

On the Formal Verification of Diffusion Phenomena in Open Dynamic Agent Networks

The paper is a contribution at the interface of social network theory and multi-agent systems. As realistic models of multi-agent systems, we assume agent networks to be open, that is, agents may join or leave the network at run-time, and dynamic, that is, the network structure may change as a result of agents actions. We provide a formal model of open dynamic agent networks (ODAN) in terms of ...

متن کامل

Modeling and Analysis of Social Activity Process

Behavior modeling has been increasingly recognized as a crucial means for disclosing interior driving forces and impact in social activity processes. Traditional behavior modeling in behavior and social sciences that mainly relies on qualitative methods is not aimed at deep and quantitative analysis of social activities. However, with the booming needs of understanding customer behaviors and so...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017