Exploring the Evolution of Node Neighborhoods in Dynamic Networks

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes

Analysis of colour-magnitude diagrams of rich LMC clusters: NGC 1831

We present the analysis of a deep colour-magnitude diagram (CMD) of NGC 1831, a rich star cluster in the LMC. The data were obtained with HST/WFPC2 in the F555W (~V) and F814W (~I) filters, reaching m_555 ~ 25. We discuss and apply a method of correcting the CMD for sampling incompleteness and field star contamination. Efficient use of the CMD data was made by means of direct comparisons of the observed to model CMDs. The model CMDs are built by an algorithm that generates artificial stars from a single stellar population, characterized by an age, a metallicity, a distance, a reddening value, a present day mass function and a fraction of unresolved binaries. Photometric uncertainties are empirically determined from the data and incorporated into the models as well. Statistical techniques are presented and applied as an objective method to assess the compatibility between the model and data CMDs. By modelling the CMD of the central region in NGC 1831 we infer a metallicity Z = 0.012, 8.75 < log(tau) < 8.80, 18.54 < (m-M)_0 < 18.68 and 0.00 < E(B-V) < 0.03. For the position dependent PDMF slope (alpha = -dlog(Phi(M))/dlog(M)), we clearly observe the effect of mass segregation in the system: for projected distances R < 30 arcsec, alpha ~ 1.7, whereas 2.2 < alpha < 2.5 in the outer regions of NGC 1831.Comment: 12 pages, 14 figure