Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

In recent years, community detection is important because members of the same community share the same concepts. For efficient community detection in a social network, the influence node plays a vital role. A node in the social network or a user that has great influence and power would have a close relationship with a core of the group, termed a community. Therefore, the status of a person is determined by the user's influence strength. That is, a user who has greater influence and strength plays a vital role in the social media community and also acts as a core in the community of the social network. Therefore, a community is a group of nodes in the complex network structure which are interlinked with one another. Effective community detection in a complex structure is a challenging task. Many studies have been done based on topological networks. The approaches are ineffective, inefficient and require more time to process. To overcome these issues, this paper proposes improving the influence nodes in complex networks by using the InfoMap with sigmoid fish swarm optimization algorithm (I-SFSO). Our proposed I-SFSO gives better accuracy rates for the data sets: 92% for Dolphin, 95% for the Facebook dataset, 96% for the Twitter data set, 94% for the YouTube data set, 93% for a karate club and 94% for football.