چگونگی بهینه سازی تأثیر شبکه های اجتماعی
Abstract
I- Introduction
II- Related Work
III- Proposed Appraoch
IV- Conclusion
References
Abstract
Online social networks are rapidly becoming an identifying feature of modern society. As online communities continue to grow, business networks of different domains also grow. As online social network begins to become increasingly integrated in our daily lives, the optimization of social network influence becomes increasingly important. In this paper, I propose to optimize influence using a modified linear threshold model. Under this model, I will focus on strategically selecting a set of users that will optimize our influence within the network. I call this type of problem as influence optimization. In this paper, I will propose a model to optimize social network influence.
INTRODUCTION
With the rapid expansion of online social networks, online social networks have become a critical medium of communication and interactions between individuals. Online social networks have provided ways to communicate strangers and friends alike. With online social networks, communication is faster than ever, and the scope of communication is much more diverse. Due to the implications of social networks, it has become necessary for companies and researchers to study and gain understanding of them. In particular, it has become very beneficial for researchers to study the diffusion of information within an online social network. While online social networks retain many properties of a regular networks such as local influence, online social networks have allowed to expand influence beyond that of the local network. Ultimately, online social networks have expanded individual scopes of influence. An important topic for online social networks is influence maximization. The objective of influence maximization is to maximize the number of individuals involved with a single idea within a network, which can range from news, gossip, and products, to recommendations and etc. This problem within networks has various applications such as marketing, advertising, and even the medical field. For example, a company might want to promote a new product and needs to select a set of users that can maximize the exposure of the advertised product. By selecting an optimal set of users, a company can maximize its profits and minimize its losses. Some popular influence diffusion models include the independent cascade (IC) and the linear threshold (LT). Both models are used to characterize how influence propagates throughout the network starting from the initial seed nodes. In this paper, I will use a propagation model that will have characteristics of both.