similarity-based approach for seed selection in diffusion model

Following the invention of the Internet and its expansion among all the people of the world, many software products, sites, games, and even hardware was presented by scientists, programmers, and manufacturers, whose use today is one of the essential and irreplaceable needs. It has become part of people’s daily lives. Along with all the ways and means of using the Internet, social networks also spread among people with great power and speed. Today, most people are familiar with social networks and use them differently according to their needs. One of the ways to spread information is using social networks. Through social networks, many users can be informed about information such as news, advertisement, and many other things. This diffusion of information among users has been investigated by the problem of influence maximization to provide a solution to spread the diffusion in the social network. JACCIM algorithm, after detecting communities and selecting candidate nodes from overlapping and non-overlapping nodes based on topology criteria including Jaccard coefficient, has selected a set of k nodes. This collection has been able to maximize the diffusion of information in the network. With experiments on the proposed algorithm, comparing it with some other algorithms, and checking the results, the proposed algorithm has shown a significant impact on its execution time.