Luca Dall'Asta

K-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases

We consider the

Nonequilibrium dynamics of language games on complex networks

k

Exploring networks with traceroute-like probes: theory and simulations

-core decomposition of network models and Internet graphs at the autonomous system (AS) level. The k-core analysis allows to characterize networks beyond the degree distribution and uncover structural properties and hierarchies due to the specific architecture of the system. We compare the

Network controllability is determined by the density of low in-degree and out-degree nodes.

k

Containing epidemic outbreaks by message­ passing techniques

-core structure obtained for AS graphs with those of several network models and discuss the differences and similarities with the real Internet architecture. The presence of biases and the incompleteness of the real maps are discussed and their effect on the

What is the real size of a sampled network? The case of the Internet

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Effective surface-tension in the noise-reduced voter model

-core analysis is assessed with numerical experiments simulating biased exploration on a wide range of network models. We find that the

Large deviations of cascade processes on graphs

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Entropy landscape and non-Gibbs solutions in constraint satisfaction problems.

-core analysis provides an interesting characterization of the fluctuations and incompleteness of maps as well as information helping to discriminate the original underlying structure.

Collaboration in social networks

The naming game is a model of nonequilibrium dynamics for the self-organized emergence of a linguistic convention or a communication system in a population of agents with pairwise local interactions. We present an extensive study of its dynamics on complex networks, that can be considered as the most natural topological embedding for agents involved in language games and opinion dynamics. Except for some community structured networks on which metastable phases can be observed, agents playing the naming game always manage to reach a global consensus. This convergence is obtained after a time generically scaling with the populations size N as t(conv) approximately N(1.4+/-0.1), i.e., much faster than for agents embedded on regular lattices. Moreover, the memory capacity required by the system scales only linearly with its size. Particular attention is given to heterogeneous networks, in which the dynamical activity pattern of a node depends on its degree. High-degree nodes have a fundamental role, but require larger memory capacity. They govern the dynamics acting as spreaders of (linguistic) conventions. The effects of other properties, such as the average degree and the clustering, are also discussed.