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In this paper a weighted complex network is used to detect communities of basketball players on the basis of their performances. A sparsification procedure to remove weak edges is also applied. In our proposal, at each removal of an edge the best community structure of the "giant component" is calculated, maximizing the modularity as a measure of compactness within communities and separation among communities. The "sparsification transition" is confirmed by the normalized mutual information. In this way, not only the best distribution of nodes into communities is found, but also the ideal number of communities as well. An application to community detection of basketball players for the NBA regular season 2020–2021 is presented. The proposed methodology allows a data driven decision making process in basketball. [ABSTRACT FROM AUTHOR]

Extracting meaningful information from short texts like tweets has proved to be a challenging task. Literature on topic detection focuses mostly on methods that try to guess the plausible words that describe topics whose number has been decided in advance. Topics change according to the initial setup of the algorithms and show a consistent instability with words moving from one topic to another one. In this paper we propose an iterative procedure for topic detection that searches for the most stable solutions in terms of words describing a topic. We use an iterative procedure based on clustering on the consensus matrix, and traditional topic detection, to find both a stable set of words and an optimal number of topics. We observe however that in several cases the procedure does not converge to a unique value but oscillates. We further enhance the methodology using semantic enrichment via Word Embedding with the aim of reducing noise and improving topic separation. We foresee the application of this set of techniques in an automatic topic discovery in noisy channels such as Twitter or social media. [ABSTRACT FROM AUTHOR]

We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study the dynamics of the commuting system of two insular regions of Italy, Sardinia and Sicily, inspected as complex networks. The authors refer to a 20-year time period and take into account three census data sets about the work and study-driven inter-municipal origin-destination movements of residential inhabitants in 1981, 1991 and 2001. Since it is likely that the number of municipalities (in this case, the vertices of the system) does not display sharp variations, the authors direct the study to the variation of the properties emerging through both a topological and a weighted network representation of commuting in the time periods indicated. [ABSTRACT FROM AUTHOR]

The authors discuss the application of statistical physics to reconstruct business networks from limited credit data. They state that the precise structure of networks must be maintained to be adjustable to diffusion of innovations which are deeply entwined with its topology. The authors note that bankruptcy can result from propagated network distress.