evolution. Such models can then be fitted to real networks, and used to generate realistic graphs or give formal explanations about their properties. In addition, our work has a wide range of applications: we can spot anomalous graphs and outliers, design better graph sampling algorithms, forecast future

networks. This paper presents the Network Simulation Cradle which allows real world network stacks to be used in a wrapper that allows the stacks protocols to be used in the ns-2 network simulator. The network stacks from the open source operating systems Linux, FreeBSD and OpenBSD are included

Network based security games, where a defender strategically places security measures on the edges of a graph to protect against an adversary, who chooses a path through a graph is an important research problem with potential for real-world impact. For example, police forces face the problem

Network based security games, where a defender strategically places security measures on the edges of a graph to protect against an ad-versary, who chooses a path through a graph is an important re-search problem with potential for real-world impact. For example, police forces face the problem

Abstract not found

Large scale real-world network data, such as social networks, Internet and Web graphs, are ubiquitous. The study of such social and information networks seeks to find patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes have a rich

these risks using only locally-derivable information. We illustrate by example that this challenge can be met if some general topological features of the connection network are known. By simulat-ing an attack propagation on two large real-world networks, we identify structural regularities in the resulting

Abstract Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the net-work of human contacts throughout the day. Recent

The so-called ‘small-world phenomenon’, observed in many real-world networks, is that there is a short path between any two nodes of a network, whose length is much smaller that the network’s size, typically growing as a logarithmic function. Several mathematical models have been defined

on this graph, yields a stationary distribution, and the corresponding random sample. This sample, however, is not uniform when network nodes have a nonuniform degree distribution. This poses a significant practical challenge since typical large scale, real-world, unstructured networks tend to have non