Title: Efficient spread of networks
Authors: Yuan-Lung Lin - Academia Sinica (Taiwan) [presenting]
Frederick Kin Hing Phoa - Academia Sinica (Taiwan)
Abstract: The growth of social networks, in combination with the increasing sophistication of Big Data tools, has led to a burgeoning interest in a rich understanding of relationships among people, institutions, and more. A relevant setting for such a study is graph theory, together with its random counterpart. Many graph models have been employed to investigate the clusters of nodes and the centrality of each cluster based on structure and attributes. The centrality of a network is one of the key measure of the importance of the nodes with respect to the rest of the nodes, such as degree, betweenness, eigenvector, and closeness. Each centrality is used for different purposes, but none of them is applicable to spread information in a network. Our interest is in spreading information efficiently in a network. We will propose a new measurement, domination centrality sets, which combines the advantages of known methodologies without their drawbacks. Besides, a new algorithm based on domination centrality sets will be proposed for the search of important nodes with effective spreading. For this purpose, we will also derive some theoretical methodologies which can help users to avoid exhaustive and time consuming computation.