DINGA: A Genetic-algorithm-based Method for Finding Important Nodes in Social Networks

Nowadays, a significant amount of studies are devoted to discovering important nodes in graph data. Social networks as graph data have attracted a lot of attention. There are various purposes for discovering the important nodes in social networks such as finding the leaders in them, i.e. the users who play an important role in promoting advertising, etc. Different criteria have been proposed in discovering important nodes in graph data. Measuring a node’s importance by a single criterion may be inefficient due to the variety of graph structures. Recently, a combination of criteria has been used in the discovery of important nodes. In this paper, we propose a system for the Discovery of Important Nodes in social networks using Genetic Algorithms (DINGA). In our proposed system, important nodes in social networks are discovered by employing a combination of eight informative criteria and their intelligent weighting. We compare our results with a manually weighted method, that uses random weightings for each criterion, in four real networks. Our method shows an average of 22% improvement in the accuracy of important nodes discovery.