Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model

Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks. Thus, approximation techniques have been developed and used to compute the measures in such scenarios. In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature. Our work offers several contributions. We highlight both the pros and cons of approximating centralities though neural learning. By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks. Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models, Machine Learning, Regression ModelComment: 8 pages, 5 tables, 2 figures, version accepted at IJCNN 2018. arXiv admin note: text overlap with arXiv:1810.1176

3D galaxy clustering with future wide-field surveys: Advantages of a spherical Fourier-Bessel analysis

Upcoming spectroscopic galaxy surveys are extremely promising to help in addressing the major challenges of cosmology, in particular in understanding the nature of the dark universe. The strength of these surveys comes from their unprecedented depth and width. Optimal extraction of their three-dimensional information is of utmost importance to best constrain the properties of the dark universe. Although there is theoretical motivation and novel tools to explore these surveys using the 3D spherical Fourier-Bessel (SFB) power spectrum of galaxy number counts $C_\ell(k,k^\prime)$, most survey optimisations and forecasts are based on the tomographic spherical harmonics power spectrum $C^{(ij)}_\ell$. We performed a new investigation of the information that can be extracted from the tomographic and 3D SFB techniques by comparing the forecast cosmological parameter constraints obtained from a Fisher analysis in the context of planned stage IV wide-field galaxy surveys. The comparison was made possible by careful and coherent treatment of non-linear scales in the two analyses. Nuisance parameters related to a scale- and redshift-dependent galaxy bias were also included for the first time in the computation of both the 3D SFB and tomographic power spectra. Tomographic and 3D SFB methods can recover similar constraints in the absence of systematics. However, constraints from the 3D SFB analysis are less sensitive to unavoidable systematics stemming from a redshift- and scale-dependent galaxy bias. Even for surveys that are optimised with tomography in mind, a 3D SFB analysis is more powerful. In addition, for survey optimisation, the figure of merit for the 3D SFB method increases more rapidly with redshift, especially at higher redshifts, suggesting that the 3D SFB method should be preferred for designing and analysing future wide-field spectroscopic surveys.Comment: 12 pages, 6 Figures. Python package for cosmological forecasts available at https://cosmicpy.github.io . Updated figures. Matches published versio

Linking haloes to galaxies: how many halo properties are needed?

Recent studies emphasize that an empirical relation between the stellar mass of galaxies and the mass of their host dark matter subhaloes can predict the clustering of galaxies and its evolution with cosmic time. In this paper we study the assumptions made by this methodology using a semi-analytical model (SAM). To this end, we randomly swap between the locations of model galaxies within a narrow range of subhalo mass (M_infall). We find that shuffled samples of galaxies have different auto-correlation functions in comparison with the original model galaxies. This difference is significant even if central and satellite galaxies are allowed to follow a different relation between M_infall and stellar mass, and can reach a factor of 2 for massive galaxies at redshift zero. We analyze three features within SAMs that contribute to this effect: a) The relation between stellar mass and subhalo mass evolves with redshift for central galaxies, affecting satellite galaxies at the time of infall. b) The stellar mass of galaxies falling into groups and clusters at high redshift is different from the mass of central galaxies at the same time. c) The stellar mass growth for satellite galaxies after infall can be significant and depends on the infall redshift and the group mass. We show that the above is true for differing SAMs, and that the effect is sensitive to the treatment of dynamical friction and stripping of gas in satellite galaxies. We find that by using the FoF group mass at redshift zero in addition to M_infall, an empirical model is able to accurately reproduce the clustering properties of galaxies. On the other hand, using the infall redshift as a second parameter does not yield as good results because it is less correlated with stellar mass. Our analysis indicates that environmental processes are important for modeling the clustering and abundance of galaxies. (Abridged)Comment: Accepted for publication in MNRAS, minor changes from version

From patterned response dependency to structured covariate dependency: categorical-pattern-matching

Data generated from a system of interest typically consists of measurements from an ensemble of subjects across multiple response and covariate features, and is naturally represented by one response-matrix against one covariate-matrix. Likely each of these two matrices simultaneously embraces heterogeneous data types: continuous, discrete and categorical. Here a matrix is used as a practical platform to ideally keep hidden dependency among/between subjects and features intact on its lattice. Response and covariate dependency is individually computed and expressed through mutliscale blocks via a newly developed computing paradigm named Data Mechanics. We propose a categorical pattern matching approach to establish causal linkages in a form of information flows from patterned response dependency to structured covariate dependency. The strength of an information flow is evaluated by applying the combinatorial information theory. This unified platform for system knowledge discovery is illustrated through five data sets. In each illustrative case, an information flow is demonstrated as an organization of discovered knowledge loci via emergent visible and readable heterogeneity. This unified approach fundamentally resolves many long standing issues, including statistical modeling, multiple response, renormalization and feature selections, in data analysis, but without involving man-made structures and distribution assumptions. The results reported here enhance the idea that linking patterns of response dependency to structures of covariate dependency is the true philosophical foundation underlying data-driven computing and learning in sciences.Comment: 32 pages, 10 figures, 3 box picture