Ricerc@Sapienza

In this paper, techniques proper to complex networks studies have been applied to analyze features of the investment styles and similarities in the Italian pension funds. The analysis has been developed through interdisciplinary approaches. First, we look at the node degree distributions; next, we consider the centrality measures, like betweenness and closeness. Results indicate that the network of funds is dense and assortative, with short path lengths. Moreover, through community detection algorithms, it is found that many funds show similar features.

The complex nature of the interlacement of economic actors is quite evident at the level of the Stock market, where any company may actually interact with the other companies buying and selling their shares. In this respect, the companies populating a Stock market, along with their connections, can be effectively modeled through a directed network, where the nodes represent the companies, and the links indicate the ownership. This paper deals with this theme and discusses the concentration of a market.

Commentary on: Martino, A, Giuliani, A, Todde, V, Bizzarri, M, Rizzi, A, 2019, “Metabolic Networks Classification
Knowledge Discovery by Information Granulation” Computers in Biology and Chemistry, pp. 107187. DOI: 10.1016/j.
compbiolchem.2019.107187

This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of which an embedding space can be built by means of symbolic histograms. In the embedding space, any Euclidean pattern recognition system can be used, possibly equipped with feature selection capabilities in order to select the most informative symbols.

Graphs are powerful structures able to capture topological and semantic information from data, hence suitable for modelling a plethora of real-world (complex) systems. For this reason, graph-based pattern recognition gained a lot of attention in recent years. In this paper, a general-purpose classification system in the graphs domain is presented. When most of the information of the available patterns can be encoded in edge labels, an information granulation-based approach is highly discriminant and allows for the identification of semantically meaningful edges.

Heavy rainfall, floods and other hydroclimatic extremes may be related to specific states of organization of the atmospheric circulation. The identification of these states and their linkage to local extremes could facilitate a physically meaningful quantification of local extremes in future climates and could allow forecasting extremes conditioned on the large-scale atmospheric state. A novel methodology is presented that combines non-linear, non-parametric methods to link heavy precipitation events (HPEs) to atmospheric circulation states.