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Increasing numbers of mobile computing devices form dynamic networks in everyday life. In such environments, nodes (i.e. laptops, PDAs, smart phones) are sparsely distributed forming a network. Such networks are often partitioned due to geographical separation or node movement. New communication paradigms using dynamic interconnectedness between people and urban infrastructure lead towards a world where digital traffic flows in small leaps as people pass each other.
Efficient forwarding algorithms for such networks are emerging, mainly based on epidemic routing protocols where messages are simply flooded when a node encounters another node. To reduce the overhead of epidemic routing, we attempt to uncover a hidden stable network structure such as social networks, which consist of a group of people forming socially meaningful relationships.
I will describe our study of patterns or information flow during epidemic spread in dynamic human networks, which shares many issues with network-based epidemiology. Properties of human contact networks such as community and weight of interactions are important aspects of epidemic spread. I will consider a model for space-time paths based on graph evolution: time-dependent networks where links between nodes are time-windows dependent. I will explore epidemic change by exploiting device connectivity traces from the real world and demonstrate the characteristics of information propagation. I will show clustering nodes with traces that form human communities. An experimental rather than theoretical approach is used in this study.
This research work is part of the EU Haggle project (for details, see my web page:see
