Jean-Charles Delvenne

TL;DR: In this paper, the authors introduce the stability of a partition, a measure of its quality as a community structure based on the clustered autocovariance of a dynamic Markov process taking place on the network.

TL;DR: In this article, the stability of a network partition is defined in terms of the statistical properties of a dy namical process taking place on the graph, and the connection between community detection and Laplacian dynamics enables them to establish dynamically motivated stability measures linked to distinct null models.

TL;DR: The connections between various features of human behavior extracted from a large mobile phone dataset are explored and it is shown that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information.

TL;DR: In this paper, the Markov Stability, a time-parametrized function defined in terms of the statistical properties of a Markov process taking place on the graph, is introduced to find multi-scale community structure in large networks.

TL;DR: In this article, the authors consider formations of autonomous agents moving in a two-dimensional space, each agent tries to maintain its distances toward a pre-specified group of other agents constant and the problem is to determine if one can guarantee that the distance between every pair of agents (even those not explicitly maintained) remains constant, resulting in the persistence of the formation shape.