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The dominant eigenvector of matrices defined by weighted links in overlay networks plays an important role in many peer-to-peer applications. Examples include trust management, importance ranking to support search, and virtual coordinate systems to facilitate managing network proximity. Robust and efficient asynchronous distributed algorithms are known only… (More)

—To understand the diffusive spreading of a product in a telecom network, whether the product is a service, handset, or subscription, it can be very useful to study the structure of the underlying social network. By combining mobile traffic data and product adoption history from one of Telenor's markets, we can define and measure an adoption… (More)

In this paper we introduce a model for analyzing the spread of epidemics in a disconnected mobile network. The work is based on an extension, to a dynamic setting, of the eigenvector centrality principle introduced by two of the authors for the case of static networks. The extension builds on a new definition of <i>connectivity matrix</i> for a highly… (More)

In this chapter we introduce a model for analyzing the spread of epidemics in a disconnected mobile network. The work is based on an extension, to a dynamic setting, of the eigenvector centrality principle introduced by two of the authors for the case of static networks. The extension builds on a new definition of connectivity matrix for a highly… (More)

Archipelago is system analysis and visualization tool which implements several methods of automated resource and security analysis for human-computer networks; this includes physical networks , social networks, knowledge networks and networks of clues in a forensic analysis. Access control, intrusions and social engineering can be discussed in the framework… (More)

We study the properties of the principal eigenvector for the adjacency matrix (and related matrices) for a general directed graph. In particular—motivated by the use of the eigenvector for estimating the " importance " of the nodes in the graph—we focus on the distribution of positive weight in this eigenvector, and give a coherent picture which builds upon… (More)