چكيده به لاتين
An online social network can be considered a collection of different individuals with various online contact patterns between them. Social forums are places in which people can contact their friends, family, and their loved ones, and share information regarding their status, and so forth. During past years with the advent of the Internet, this kind of complex yet ever-growing and fresh networks have enhanced at an unimaginable pace and dominated the market. The growth of successful social networks has allowed marketers to grow their businesses by attracting influential users and customers through new marketing strategies. Different marketing techniques such as word-of-mouth or viral marketing approaches have paved the way and replaced traditional methods. These new strategies have shown better performance in advertising the products, attracting customers, and increasing brand awareness specifically for relatively smaller companies with limited resources. Thus, to maximize the influence of social networks and identify the effective actors to replace them as the primary ones in networks for viral marketing, some models were developed. The next important issue after identifying the actors is to come up with valuing methods to evaluate the amount of impact these actors have in the created influence. In the current study, the methods with which the active actors in social networks are valued, have been studied. However, the available approaches, none of them have considered the diffusion coefficient of the active actors. Moreover, including more than one active actor (i.e., several actors) can increase the chances of overlapping, which does not add up to any value. On the other hand, it is clear that in act of distribution, the more the actors are exposed to the influential actors, the more likely they will accept the product. Therefore, determining the actors’ values altogether, along with calculating the overlapping and synergy values of actors in the set of primary nodes is achieved in the present study. This model was implemented on the graph by simulating a social network graph, and then the coefficient of each actor was determined. Next, to evaluate the performance of the developed model on larger datasets, the available graphs literature and data websites were employed. Finally, the effect of parameters such as the number of initial actors and the effectiveness threshold in determining these coefficients and the value of actors was investigated.
