We describe techniques for the robust detection of community structure in some classes of
time-dependent networks. Specifically, we consider the use of statistical null models for …

The increasing interest in complex networks research has been motivated by intrinsic
features of this area, such as the generality of the approach to represent and model virtually …

The importance of discrete and combinatorial mathematics continues to increase as the
range of applications to computer science, electrical engineering, and the biological …

Using the eigenvalues and eigenvectors of correlations matrices of some of the main
financial market indices in the world, we show that high volatility of markets is directly linked …

Networks are a convenient way to represent complex systems of interacting entities. Many
networks contain “communities” of nodes that are more densely connected to each other …

We consider the problem of breaking a multivariate (vector) time series into segments over
which the data is well explained as independent samples from a Gaussian distribution. We …

We study how to assess the potential benefit of diversifying an equity portfolio by investing
within and across equity sectors. We analyse 20 years of US stock price data, which …

Daily correlations among 322 S&P 500 constituent stocks are investigated by means of
correlation-based (CB) network. By using the heterogeneous time scales, we identify global …

The singular value decomposition (SVD) is a popular matrix factorization that has been used
widely in applications ever since an efficient algorithm for its computation was developed in …

This paper investigates the geographies of high frequency trading. Today shares shift hands
within microseconds, giving rise to a form of financial geographies termed algorithmic …