An Algorithm For Discovering Influential Nodes In Weighted Social Networks

With threats such as AIDS,SARS,influenza and other infectious diseases on human health are growing, and terrorism known as the "21st -century political plague" is quietly filled, such as how infectious diseases to be effectively and quickly brought under control in a particular social networks? How to accurately locate the leaders of terrorist groups in the network? Which are important and keys in the rapid arrests? And so other issues are also closely related to the discovery and evaluation of important nodes in a social network. Since the discovery of the important nodes in social networks, not only in the field of human society,but also in national politics and other fields, has great practical value and of great practical significance, so in recent years the related work become a hot topic in the domestic and foreign scholars.Based on the research of the current methods of complex-network important node discovery, we find that those algorithms are usually based on the view of global sorted importance for all nodes in the network, but very few scholars can research on the local relative importance of nodes in the network. In addition, nodes and edges of social network for the network information dissemination and diffusion have a very important influence, so when assessing and finding the importance of social network's nodes, we should pay more attention on the right side of the node weights and properties of the network.Aiming at these problems, we raise an important social-networks discovery algorithm which based on the weighted node. The algorithm based on attribute of nodes and edges in social networks creates a weighted social network model,the relative importance of the theory and principles of network topology convergence between nodes weighted social network co-discovered the important nodes.In the follow experiments, we use the social-networks analysis software Gephi to process networks, and a programming language Java to run these algorithms. The data sets of the China domestic flights between cities and the data sets from a trusted network of social networking from Advogato, are used in the experiments for multi-group comparison of experimental results to show that the proposed algorithm can better discover weighted important nodes in the network and is a practical algorithm.