Choosing the optimal set of influential people has become an attractive problem in complex networks. This problem is broken into two sub-problems: (1) finding the influential nodes and ranking them based on the individual influence of each node (2) finding a group of nodes to achieve the maximum spread in the network. In this thesis, both sub-problems have been examined and a method for measuring the spread power of influential nodes in the network and selecting the optimal group from them has been presented. In the proposed method, first the input network is divided into different communities. Then, the edges of each community are weighted and in each of the communities, the spreading power of the nodes is measured and ranked. Finally, a group of influential nodes were selected to start the publishing process. Data sets of real networks have been used to evaluate the methods. The proposed method was compared with other previously known methods in two parts. In the first part, the accuracy of the method in measuring the spread power of network nodes is compared based on the resolution and similarity parameters, and in the second part, the proposed method is compared with other methods in terms of the spread amount of influence of the selected set. The obtained results show the significant superiority of the proposed method in all three evaluation criteria over other methods.
