Research On Algorithms For Measuring And Discovering Important Nodes In Social Networks

In recent years, with the development of the Internet, social networks are increasingly becoming the focus of attention. It is becoming an important issue to find important node. In social networks, knowing the mutual influence between nodes, understanding the importance of nodes of network, it has a very important part to analysis of the frame of social networks and research network node information.In this paper, we research on the importance of nodes evaluation and important node applications in social networks, the main work is as follows:1, To evaluate the importance of node in social networks, the paper proposes the user ranking algorithm based on iterative. The algorithm not only take into account the a characteristic properties of nodes or the affect of the adjacent nodes, but also taking into account the impact of the degree of importance of the nodes connected to a network and the importance of non-adjacent nodes for the impact of evaluation nodes. The algorithm is to analyze the importance of nodes from the local network to the whole network, to evaluate the importance of nodes in social networks.2, Based on several methods to evaluate the importance of the nodes, the paper proposes a comprehensive model of social networks to measure the importance of nodes. This model overcomes the mentioned methods to measure the importance of nodes is one-sidedness, from multiple attributes and the overall structure of the network nodes to evaluate the importance of nodes in the social network. It is comprehensive and integrated evaluate the importance of nodes calculation.3, The paper proposes clustering algorithm based on field theory in social networks. In the existing social networks clustering algorithm, the methods is proposed a social network clustering based on data field theory learning ideas. The method is based on an assessment earlier in this article on the importance of nodes, using the data field theory ideas to divide the node cluster structure of the network.