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Influence Maximization is one of the important research topics in social networks which has many applications, e.g., in marketing, politics and social science. The goal of Influence Maximization is to select a limited number of vertices (called seed set) in a social graph, so that upon their direct activation, the maximum number of vertices is activated through social interaction of the seed set with...
Influence and information propagation is an important field in social network analysis. Propagation models are main tools in predicting propagation behaviour. Classic propagation models Linear Threshold, Independent Cascade and their extension are based on network structure and actions of users in time. We propose to use network context to improve propagation prediction. Experiments from real datasets...
Most of the relational information visualization systems use graphs to represent information. In some applications it is needed to make acyclic graphs, so we have to solve the Feedback Set Problem (FSP). FSP is an important problem in graph theory and asks for a minimum sized subset of arcs of a directed graph whose removal makes the graph acyclic. In this paper we propose a genetic algorithm for...
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