Influence Maximization on Multi-Phased Multi-Layered Network

With the development of social network, people start to pay attention to its internal information diffusion and propagation process. However, most state-of-the-art works mainly analyzed single-phased and single-layered network. In our project, we study the information spread on multilayered network. We focus on one specific event the information of the discovery of a new particle spreading in Twitter. The participated users and their corresponding actions have formed a multi-layered, multi-phased network. We explored different methods to aggregate multi-layered networks. Using different influence maximization algorithms and different methods to assign edge probability on the aggregated network in different phases, we are able to analyze the important nodes and layers in the given dataset. The experiment results show the effectiveness of our aggregation strategy on our multi-phased and multi-layered network.