Complexity, Centralization, and Fragility in Economic Networks

Trade networks, across which countries distribute their products, are crucial components of the globalized world economy. Their structure is strongly heterogeneous across products, given the different features of the countries which buy and sell goods. By using a diversified pool of indicators from network science and product complexity theory, we quantitatively confirm the intuition that, overall, products with higher complexity -- i.e., with larger technological content and number of components -- are traded through a more centralized network -- i.e., with a small number of countries concentrating most of the export flow. Since centralized networks are known to be more vulnerable, we argue that the current composition of production and trading is associated to high fragility at the level of the most complex -- thus strategic -- products

Topology comparison of Twitter diffusion networks effectively reveals misleading information

In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that misleading information spreads faster, deeper and more broadly than factual information on social media, where echo chambers, algorithmic and human biases play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of diffusion networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of misleading articles, including false news intended to harm, satire intended to make people laugh, click-bait news that may be entirely factual or rumors that are unproven. We carried out an extensive comparison of these networks using several alignment-free approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these techniques allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream news, compared to those sharing misleading news, tend to shape diffusion networks with subtle yet systematic differences which might be effectively employed to identify misleading and harmful information.Comment: A revised new version is available on Scientific Report

Metrics for network comparison using egonet feature distribution

Identifying networks with similar characteristics in a given ensemble, or detecting pattern discontinuities in a temporal sequence of networks, are two examples of tasks that require an effective metric capable of quantifying network (dis)similarity. Here we propose a method based on a global portrait of graph properties built by processing local nodes features. More precisely, a set of dissimilarity measures is defined by elaborating the distributions, over the network, of a few egonet features, namely the degree, the clustering coefficient, and the egonet persistence. The method, which does not require the alignment of the two networks being compared, exploits the statistics of the three features to define one- or multi-dimensional distribution functions, which are then compared to define a distance between the networks. The effectiveness of the method is evaluated using a standard classification test, i.e., recognizing the graphs originating from the same synthetic model. Overall, the proposed distances have performances comparable to the best state-of-the-art techniques (graphlet-based methods) with similar computational requirements. Given its simplicity and flexibility, the method is proposed as a viable approach for network comparison tasks

Wladimir Vogel e l’accoglienza dei musicisti nella Svizzera italiana tra le due guerre

Since the Risorgimento period, Italian Switzerland was known as a place that sometimes welcomed people persecuted by illiberal regimes. One of those who found refuge there during the Nazi-fascist period was Wladimir Vogel, a Russo-German composer who was taken in, first at Comologno (in the Onsernone Valley) and then at Ascona, in the home of Wladimir Rosenbaum and Aline Valangin. Having participated in the Workers’ Music Movement, he had to flee Berlin when Hitler came to power, and until after the war he was given only a Swiss tourist visa, not a work permit. It was a difficult situation, but he was helped by Aline Valangin, who later became his wife. She assisted him in organizing, at Comologno on 14 August 1936, a music course that hosted Willi Reich in a series of classes on twelve-tone music – the first occasion in Switzerland for examining Schoenberg’s method. Having come in contact with the Italian Swiss Radio in Lugano, Vogel was able to present some of his works. From that position, he was able to direct his efforts toward Italy, where he established an important relationship with Luigi Dallapiccola. The most significant initiative took place in Locarno on 12 December 1948, at the preparatory meeting for the First International Congress on Twelve-Tone Music, which was to be held in Milan, 4–7 May 1949, and which Vogel organized in collaboration with Riccardo Malipiero; this was the first occasion for an international meeting of “radical” composers.Since the Risorgimento period, Italian Switzerland was known as a place that sometimes welcomed people persecuted by illiberal regimes. One of those who found refuge there during the Nazi-fascist period was Wladimir Vogel, a Russo-German composer who was taken in, first at Comologno (in the Onsernone Valley) and then at Ascona, in the home of Wladimir Rosenbaum and Aline Valangin. Having participated in the Workers’ Music Movement, he had to flee Berlin when Hitler came to power, and until after the war he was given only a Swiss tourist visa, not a work permit. It was a difficult situation, but he was helped by Aline Valangin, who later became his wife. She assisted him in organizing, at Comologno on 14 August 1936, a music course that hosted Willi Reich in a series of classes on twelve-tone music – the first occasion in Switzerland for examining Schoenberg’s method. Having come in contact with the Italian Swiss Radio in Lugano, Vogel was able to present some of his works. From that position, he was able to direct his efforts toward Italy, where he established an important relationship with Luigi Dallapiccola. The most significant initiative took place in Locarno on 12 December 1948, at the preparatory meeting for the First International Congress on Twelve-Tone Music, which was to be held in Milan, 4–7 May 1949, and which Vogel organized in collaboration with Riccardo Malipiero; this was the first occasion for an international meeting of “radical” composers.Since the Risorgimento period, Italian Switzerland was known as a place that sometimes welcomed people persecuted by illiberal regimes. One of those who found refuge there during the Nazi-fascist period was Wladimir Vogel, a Russo-German composer who was taken in, first at Comologno (in the Onsernone Valley) and then at Ascona, in the home of Wladimir Rosenbaum and Aline Valangin. Having participated in the Workers’ Music Movement, he had to flee Berlin when Hitler came to power, and until after the war he was given only a Swiss tourist visa, not a work permit. It was a difficult situation, but he was helped by Aline Valangin, who later became his wife. She assisted him in organizing, at Comologno on 14 August 1936, a music course that hosted Willi Reich in a series of classes on twelve-tone music – the first occasion in Switzerland for examining Schoenberg’s method. Having come in contact with the Italian Swiss Radio in Lugano, Vogel was able to present some of his works. From that position, he was able to direct his efforts toward Italy, where he established an important relationship with Luigi Dallapiccola. The most significant initiative took place in Locarno on 12 December 1948, at the preparatory meeting for the First International Congress on Twelve-Tone Music, which was to be held in Milan, 4–7 May 1949, and which Vogel organized in collaboration with Riccardo Malipiero; this was the first occasion for an international meeting of “radical” composers.Since the Risorgimento period, Italian Switzerland was known as a place that sometimes welcomed people persecuted by illiberal regimes. One of those who found refuge there during the Nazi-fascist period was Wladimir Vogel, a Russo-German composer who was taken in, first at Comologno (in the Onsernone Valley) and then at Ascona, in the home of Wladimir Rosenbaum and Aline Valangin. Having participated in the Workers’ Music Movement, he had to flee Berlin when Hitler came to power, and until after the war he was given only a Swiss tourist visa, not a work permit. It was a difficult situation, but he was helped by Aline Valangin, who later became his wife. She assisted him in organizing, at Comologno on 14 August 1936, a music course that hosted Willi Reich in a series of classes on twelve-tone music – the first occasion in Switzerland for examining Schoenberg’s method. Having come in contact with the Italian Swiss Radio in Lugano, Vogel was able to present some of his works. From that position, he was able to direct his efforts toward Italy, where he established an important relationship with Luigi Dallapiccola. The most significant initiative took place in Locarno on 12 December 1948, at the preparatory meeting for the First International Congress on Twelve-Tone Music, which was to be held in Milan, 4–7 May 1949, and which Vogel organized in collaboration with Riccardo Malipiero; this was the first occasion for an international meeting of “radical” composers

Community analysis in directed networks: In-, out-, and pseudocommunities

When analyzing important classes of complex interconnected systems, link directionality can hardly be neglected if a precise and effective picture of the structure and function of the system is needed. If community analysis is performed, the notion of “community” itself is called into question, since the property of having a comparatively looser external connectivity could refer to the inbound or outbound links only or to both categories. In this paper, we introduce the notions of in-, out-, and in-/out-community in order to correctly classify the directedness of the interaction of a subnetwork with the rest of the system. Furthermore, we extend the scope of community analysis by introducing the notions of in-, out-, and in-/out-pseudocommunity. They are subnetworks having strong internal connectivity but also important interactions with the rest of the system, the latter taking place by means of a minority of its nodes only. The various types of (pseudo-)communities are qualified and distinguished by a suitable set of indicators and, on a given network, they can be discovered by using a “local” searching algorithm. The application to a broad set of benchmark networks and real-world examples proves that the proposed approach is able to effectively disclose the different types of structures above defined and to usefully classify the directionality of their interactions with the rest of the system

Uncovering the Structure of Criminal Organizations by Community Analysis: The Infinito Network

Criminal organizations tend to be clustered to reduce risks of detection and information leaks. Yet, the literature has so far neglected to explore the relevance of subgroups for their internal structure. The paper focuses on a case study drawing from a large law enforcement operation (\u201dOperazione Infinito\u201d). It applies methods of community analysis to explore the structure of a \u2019Ndrangheta (a mafia from Calabria, a southern Italian region) network representing the individuals\u2019 co-participation in meetings. The results show that the network is significantly clustered and that communities are partially associated with the internal organization of the \u2019Ndrangheta into different locali (similar to mafia families). The implications of these findings on the interpretation of the structure and functioning of the criminal network are discussed

Communities in criminal networks: A case study

Criminal organizations tend to be clustered to reduce risks of detection and information leaks. Yet, the literature exploring the relevance of subgroups for their internal structure is so far very limited. The paper applies methods of community analysis to explore the structure of a criminal network representing the individuals\u2019 co-participation in meetings. It draws from a case study on a large law enforcement operation (\u201cOperazione Infinito\u201d) tackling the \u2018Ndrangheta, a mafia organization from Calabria, a southern Italian region. The results show that the network is indeed clustered and that communities are associated, in a non-trivial way, with the internal organization of the \u2018Ndrangheta into different \u201clocali\u201d (similar to mafia families). Furthermore, the results of community analysis can improve the prediction of the \u201clocale\u201d membership of the criminals (up to two thirds of any random sample of nodes) and the leadership roles (above 90% precision in classifying nodes as either bosses or non-bosses). The implications of these findings on the interpretation of the structure and functioning of the criminal network are discusse

Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis

The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activitie

Finding and testing network communities by lumped Markov chains

Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition which is based on a significance threshold. By means of a lumped Markov chain model of a random walker, a quality measure called "persistence probability" is associated to a cluster. Then the cluster is defined as an "$\alpha$-community" if such a probability is not smaller than $\alpha$. Consistently, a partition composed of $\alpha$-communities is an "$\alpha$-partition". These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired $\alpha$-level allows one to immediately select the $\alpha$-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the significance of each single community. Given its ability in individually assessing the quality of each cluster, this approach can also disclose single well-defined communities even in networks which overall do not possess a definite clusterized structure