Detailed-level modelling of influence spreading on complex networks.

The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures.

Resilience of Multi-Layer Communication Networks.

Advances in the future communication technologies and capabilities of new services in heterogeneous network systems have increased the need for modelling and analysing various aspects of both the resilience of networked systems and usability from the user's point of view. We extend the traditional network reliability theory to cover a wider scope of quality requirements and applications. The proposed method can be used to model the resilience of different structured networks, and the quality of information services.

Influence spreading model used to analyse social networks and detect sub-communities.

A dynamic influence spreading model is presented for computing network centrality and betweenness measures. Network topology, and possible directed connections and unequal weights of nodes and links, are essential features of the model. The same influence spreading model is used for community detection in social networks and for analysis of network structures.