Finding community structure in networks using the eigenvectors of matrices

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.Comment: 22 pages, 8 figures, minor corrections in this versio

Expression and regulation of nonsteroidal anti-inflammatory drug–activated gene (NAG-1) in human and mouse tissue

AbstractBackground & Aims: Nonsteroidal anti-inflammatory drugs (NSAIDs) induce NSAID-activated gene 1 (NAG-1), which has proapoptotic and antitumorigenic activities. However, NAG-1 expression and its relationship with apoptosis in human and mouse intestinal tract have not been determined. Methods: NAG-1 expression in human and mouse tissue was determined by immunohistochemistry, and apoptosis was estimated by in situ apoptosis detection. Apoptosis in NAG-1 overexpressing HCT-116 cells was examined with flow cytometry after cell sorting by green fluorescence protein. NAG-1 regulation in mouse cells was examined by Northern blot analysis, comparing sulindac-treated and nontreated mice. Results: Apoptosis was higher in NAG-1 overexpressing cells compared with controls. Human NAG-1 protein was localized to the colonic surface epithelium where cells undergo apoptosis, and higher expression was observed in the normal surface epithelium than in most of the tumors. This localization and lower expression in tumors was similar to that in the Min mouse, in which NSAIDs were also shown to regulate the expression of NAG-1 in mouse cells. Sulindac treatment of mice increased the NAG-1 expression in the colon and liver. Conclusions: Based on these results, we propose that NAG-1 acts as a mediator of apoptosis in intestinal cells and may contribute to cancer chemoprevention by NSAIDs.GASTROENTEROLOGY 2002;122:1388-139

Employing with conviction: The experiences of employers who actively recruit criminalised people

Atherton, P., & Buck, G. Employing with conviction: The experiences of employers who actively recruit criminalised people. Probation Journal, 68(2), pp. 186-205. Copyright © [2021] (Copyright Holder). Reprinted by permission of SAGE Publications.In England and Wales, criminal reoffending costs £18 billion annually. Securing employment can support desistance from crime, but only 17% of ex-prisoners are employed a year after release. Understanding the motivations of employers who do recruit criminalised people therefore represents an important area of inquiry. This article draws upon qualitative interviews with twelve business leaders in England who proactively employ criminalised people. Findings reveal that inclusive recruitment can be (indirectly) encouraged by planning policies aimed to improve social and environmental well-being and that employers often work creatively to meet employees’ additional needs, resulting in commercial benefits and (re)settlement opportunities

Local Causal States and Discrete Coherent Structures

Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded rigorous analysis and empirical prediction, with progress being made only recently. As a step in this, we present a formal theory of coherent structures in fully-discrete dynamical field theories. It builds on the notion of structure introduced by computational mechanics, generalizing it to a local spatiotemporal setting. The analysis' main tool employs the \localstates, which are used to uncover a system's hidden spatiotemporal symmetries and which identify coherent structures as spatially-localized deviations from those symmetries. The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates. As such, it offers an unsupervised approach to discover and describe coherent structures. We illustrate the approach by analyzing coherent structures generated by elementary cellular automata, comparing the results with an earlier, dynamic-invariant-set approach that decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht

Minding impacting events in a model of stochastic variance

We introduce a generalisation of the well-known ARCH process, widely used for generating uncorrelated stochastic time series with long-term non-Gaussian distributions and long-lasting correlations in the (instantaneous) standard deviation exhibiting a clustering profile. Specifically, inspired by the fact that in a variety of systems impacting events are hardly forgot, we split the process into two different regimes: a first one for regular periods where the average volatility of the fluctuations within a certain period of time is below a certain threshold and another one when the local standard deviation outnumbers it. In the former situation we use standard rules for heteroscedastic processes whereas in the latter case the system starts recalling past values that surpassed the threshold. Our results show that for appropriate parameter values the model is able to provide fat tailed probability density functions and strong persistence of the instantaneous variance characterised by large values of the Hurst exponent is greater than 0.8, which are ubiquitous features in complex systems.Comment: 18 pages, 5 figures, 1 table. To published in PLoS on

Improving the Louvain Algorithm for Community Detection with Modularity Maximization

International audienc

Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Background: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. Results: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. Conclusions: The novel computational paradigm provides unique “fingerprints” by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers. Background Glioblastoma are highly invasive brain tumors. Th