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

Nowadays, a significant amount of studies are devoted to the discovery of important nodes in graph data. social networks, as graph data, have attracted much 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 the important nodes in graph data. measuring a node’s importance by a single criterion may be inefficient due to the variety in the graph structures. recently, a combination of criteria has been used in the discovery of the 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, the 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 proposed method shows an average of 22% improvement in the accuracy of discovery of important nodes.