One of the central questions in neuroscience is how communication in the brain is organized
under normal conditions and how this architecture breaks down in neurological disease. It …

We survey the concept of assortativity, starting from its original definition by Newman in
2002. Degree assortativity is the most commonly used form of assortativity. Degree …

Serious epidemics, both in cyber space as well as in our real world, are expected to occur
with high probability, which justifies investigations in virus spread models in (contact) …

Improving robustness of complex networks is a challenge in several application domains,
such as power grids and water management networks. In such networks, high robustness …

We study the robustness of networks under node removal, considering random node failure,
as well as targeted node attacks based on network centrality measures. Whilst both of these …

The interplay between disease dynamics on a network and the dynamics of the structure of
that network characterizes many real-world systems of contacts. A continuous-time adaptive …

Assortativity measures the tendency of a vertex in a network being connected by other
vertexes with respect to some vertex-specific features. Classical assortativity coefficients are …

For a given graph G, each partition of the vertices has a modularity score, with higher values
indicating that the partition better captures community structure in G. The modularity q∗(G) of …

In this work, we propose a novel robustness measure for networks, which we refer to as
Effective Resistance Centrality of a vertex (or an edge), defined as the relative drop of the …

We obtain closed form expressions for the expected conditional degree distribution and the
joint degree distribution of the linear preferential attachment model for network growth in the …