Marketing impact on diffusion in social networks

Abstract

The paper proposes a way to add marketing into the standard threshold model of social networks. Within this framework, the paper studies logical properties of the influence relation between sets of agents in social networks. Two different forms of this relation are considered: one for promotional marketing and the other for preventive marketing. In each case a sound and complete logical system describing properties of the influence relation is proposed. Both systems could be viewed as extensions of Armstrong's axioms of functional dependency from the database theory. Introduction Social NetworksIn this paper we study how diffusion in social networks could be affected by marketing. Diffusion happens when a product or a social norm is initially adopted by a small group of agents who later influence their peers to adopt the same product. The peers influence their peers, and so on. There are two most commonly used models of diffusion: the cascading model and the threshold model. In the cascading model [12,8] the behaviour of agents is random and the peer influence manifests itself in a change of a probability of an agent to adopt the product. In the threshold model [16,9,7,1], originally introduced by Granovetter [6] and Schelling [13], the behavior of the agents is deterministic.The focus of this paper is on the threshold model of diffusion of a given product. In this model, there is a threshold value θ(a) associated with each agent a and an influence value w(a, b) associated with each pair of agents a and b. Informally, the threshold value θ(a) represents the resistance of agent a to adoption of the product and the influence value w(a, b) represents the peer pressure that agent a puts on agent b upon adopting the product. If the total

Identifying target audience on enterprise social network

“…Tang et al (2015) analyzed the effect of social similarity on retweet behavior based on a Weibo data set and proposed a novel model for predicting individual retweet behavior. Naumov and Tao (2017) studied logical properties of the influence relation between sets of agents in social networks and proposed a way to add marketing to the standard threshold model of social networks.…”

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“…However, neither of these systems capture principles similar to our Partition axiom. Naumov and Tao [18,19] used Armstrong's axioms to describe influence in social networks. They considered relation A b B that stands for "given marketing budget b, group of agents A can influence group of agents B" and gave an Armstronglike axioms for this relation.…”

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“…The finding was that rumors spread faster in homogeneous networks than in heterogeneous networks, while the diffusion scales of rumor spread in the two networks are exactly the opposite. Naumov and Tao (2017) added marketing into the standard threshold model of social networks and studied properties of the influence relation in social networks.…”