چكيده به لاتين
One of the most important issues in social networks is evaluation of information diffusion methods and algorithms. Social networks have different types. From small family networks to very large social networks that allow hundreds of millions of users worldwide to produce and use content. For this reason, researchers in recent years have paid much attention to understanding, predicting, and modeling social networks. Because today most of the events, communications, interests, and so on are happening on social networks. One of the important questions in information diffusion is how many and which members of the network play an important role in the information diffusion process? The problem is that which subset of influential nodes in a network will be most helpful in diffusion information. For example, a company initially targets a small number of influencers on the network by giving them a free product sample and hopes that users will first recommend the product to their friends, and their friends will also influence their friends. And the process will continue. Many people buy or sell goods through word of mouth marketing. The topic dealt with in this thesis is the average influence of the individuals selected as an indicator to measure the optimal response to the problem of the number of nodes affecting a network. The average impact acts as an indicator as a two-dimensional factor in evaluating the optimal number in the problem of maximum impact on the network. On the one hand, an increase in the number of nodes will help increase the impact and lower the average. This causes the total number of nodes selected to influence the network to be no more than Specified number. In this thesis, a new algorithm is presented to evaluate the claim, with changes in the overall particle swarm optimization (PSO) algorithm. The results obtained from the two-objective PSO algorithm show that the number obtained from the algorithm is based on the mean impact reliability and the mean impact can be used as an indicator of information diffusion on social networks.
