Abstract
In this paper we consider a fundamental problem in the area of viral marketing, called T{\scriptsize ARGET} S{\scriptsize ET} S{\scriptsize ELECTION} problem. In a a viral marketing setting, social networks are modeled by graphs with potential customers of a new product as vertices and friend relationships as edges, where each vertex is assigned a threshold value . The thresholds represent the different latent tendencies of customers (vertices) to buy the new product when their friend (neighbors) do. Consider a repetitive process on social network where each vertex is associated with two states, active and inactive, which indicate whether is persuaded into buying the new product. Suppose we are given a target set . Initially, all vertices in are inactive. At time step 0, we choose all vertices in to become active. Then, at every time step , all vertices that were active in time step remain active, and we activate any vertex if at least of its neighbors were active at time step . The activation process terminates when no more vertices can get activated. We are interested in the following optimization problem, called T{\scriptsize ARGET} S{\scriptsize ET} S{\scriptsize ELECTION}: Finding a target set of smallest possible size that activates all vertices of . There is an important and well-studied threshold called strict majority threshold, where for every vertex in we have and is the degree of in . In this paper, we consider the T{\scriptsize ARGET} S{\scriptsize ET} S{\scriptsize ELECTION} problem under strict majority thresholds and focus on three popular regular network structures: cycle permutation graphs, generalized Petersen graphs and torus cordalis.
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