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 $v$ is assigned a threshold value $θ(v)$. 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 $(G,θ)$ where each vertex $v$ is associated with two states, active and inactive, which indicate whether $v$ is persuaded into buying the new product. Suppose we are given a target set $S\subseteq V(G)$. Initially, all vertices in $G$ are inactive. At time step 0, we choose all vertices in $S$ to become active. Then, at every time step $t>0$, all vertices that were active in time step $t-1$ remain active, and we activate any vertex $v$ if at least $θ(v)$ of its neighbors were active at time step $t-1$. 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 $S$ of smallest possible size that activates all vertices of $G$. There is an important and well-studied threshold called strict majority threshold, where for every vertex $v$ in $G$ we have $θ(v)=\lceil{(d(v) +1)/2}\rceil$ and $d(v)$ is the degree of $v$ in $G$. 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.