Abstract: Public transportation system must cope with increased demand in exceptional crowd gathering such as concerts, football matches or other localized events. Here we study the emergence of delays and the collapse of a public transport network under situation of stress. A simplified model to simulate the mobility of users through the public transport system of the metropolitan area of Barcelona. The city is divided in cells represented as nodes in a multilayer network. Each of the layers stands for transport modality interconnected in the nodes and allowing an extra-layer for pedestrian movements. The transport vehicles run through the corresponding layers, while new trips are generated at a given rate producing the demand for transport that matches in origin and destination the empirical data from the city as obtained from surveys. The agents then travel through the network using two different routing protocols: either following the shortest paths, or by an adaptive mechanism that tries to avoid congested areas. We test how the network behaves when a large amount of people are injected in a fixed point of the city as it would occur in a special event This sudden increase on the demand leads to the appearance of delays and queues in the system with a dependence on the topology of the network and the distance from the injection point. The reaction of the system depends on the part of the city where the agents are generated. While some parts of the city are highly connected and are able to handle huge amount of people, there are others poorly connected that require a lot of time to redistribute the agents. We also test how the system reacts to a strike with less vehicles running, and which is the minimum amount of vehicles to avoid the collapse.

Abstract: The understanding of technological innovation's patterns is crucial for both the theory of economic growth and practical applications in research and development. Yet a precise characterization of breakthrough inventions has not been fully investigated. We address this issue using a large-scale data-mining and network approach on patent data. We extract from US Patent Office raw data an open consolidated database which includes detailed patent information, technological classifications, citation links, and abstract texts. This yields a database of around 4.10^6 patents on a time range from 1976 to 2012. We aim to capture the semantic information contained in texts which has been shown to be complementary to classification data. To do that, we extract relevant n-gram keywords and obtain for each year a semantic network based on co-occurrences. The multi-objective optimization of network modularity and size is performed on network construction parameters (filtering thresholds) through high performance computing. We obtain for each year a multi-layer network, containing semantic community relations, technological classes relations and citation relations between patents. The mining of network layers yields interesting results, such as an increase in time of patent semantic originality combined with a counter-intuitive loss of class-level interdisciplinarity. This corroborates the stylized facts of both invention refinement and specialization in time. Citation-level interdisciplinarity is investigated by combining the different layers. Finally, we plan further work towards the use of these heterogeneous features produced by multi-layer network analysis into machine learning models to predict success and breakthrough level of inventions. Our contribution to the study of socio-technical complex systems is thematic, with the construction of an open-access large scale consolidated patent database and insights into the temporal evolution of inventions, as well as methodological with a technique that can be generated to any network whose nodes contain a textual description.

Abstract: We investigate the human mobility networks using a lifelog dataset that sleeping time was recorded and a Twitter dataset that contains 150 million geotagged tweets around historical incidents. Over the last several years, we experienced critical natural disasters as 2011 Fukushima and 2016 Kumamoto earthquakes in Japan. The social turmoil was triggered by artificial incidents as 2013 Boston Marathon, 2015 Paris, 2016 Brussels, 2015-2015 Istanbul and Ankara bombings. In this paper, we compare human mobility networks before and after the incidents. Only 5% of all tweets have geotag information. However, we can track individual human mobility using the geotagged tweets because the twitter users who attach geotag tend to attach it always. We confirm that Japanese population movement is reproduced with high accuracy, &R^2=0.971&, using the lifelog dataset and the geotagged tweets. We also show that land price can be estimated from number of tourists in each commercial area in order to estimate economic losses resulting from the incidents. Next, we detect human mobility networks before and after the incidents. The networks are displayed by connecting with edge the places (nodes) that many tourists moved. Just after the incidents the number of tourists dropped drastically near the center place of incidents and commercial areas, and these land prices were damaged. Therefore, the edges with these areas are cut from the networks. The damaged networks recover in several months. Faunally, we introduce a tourist diffusion model on the networks with three parameters that are incident size (number of the dead and the injured), distance from the incident center place, time-lapse from the incident. We can simulate numerically change of human mobility patterns after the incidents.

Abstract: Despite the presence of increasing pressures towards globalisation, multiculturality, i.e. the tendency of individuals to form groups characterised by distinct sets of cultural traits, remains one of the salient aspects of human societies. Based on the two mechanisms of homophily and social influence, the classical model for the dissemination of cultures proposed by Axelrod predicts the existence of a fragmented regime where different cultures can coexist in a social network. However, in such model the multicultural regime is achievable only when a high number of cultural traits is present, and is very sensitive to the presence of spontaneous mutations of agents' traits. As a consequence, understanding how social fragmentation is able to self-sustain and thrive in many different contexts is still an open problem. In real systems, social influence is inherently organised in layers, meaning that individuals tend to diversify their connections according to the topic on which they interact. In this work we show that the persistence of multiculturality observed in real-world social systems is a natural consequence of the layered organisation of social influence. We find that the critical number of cultural traits that separates the monocultural and the multicultural regimes depends on the redundancy of pairwise connections across layers. Surprisingly, for low values of structural redundancy the system is always in a multicultural state, independently on the number of traits, and is robust to the presence of cultural drift. Moreover, we show that layered social influence allows the coexistence of different levels of consensus on different topics. The insight obtained from simulations on synthetic graphs are confirmed by the analysis of two real-world social networks, where the multicultural regime persists even for a very small number of cultural traits, suggesting that the layered organisation of social interactions might indeed be at the heart of multicultural societies.

Abstract: Communities are a universal property of complex networks as they are found in a large variety of systems ranging from brain networks to collaboration networks. Despite the large attention that has been given to community detection algorithms, the mechanisms underlying the emergence of communities have not been widely explored. We show that the triadic closure mechanism which drives the evolution of social networks and according to which two individuals have a high probability to connect after having been introduced to each other by a mutual acquaintance, naturally leads to the emergence of fat-tailed distributions of node degree, high clustering coeffcients and community structure as long as the network link density is not too high (sparse network). Moreover we show that this mechanism is able to explain the emergence of communities also in the so called multiplex networks, networks where individuals are connected via interactions of different nature (layers), and whose structure cannot be described by a single adjacency matrix. Interestingly the communities in this family of networks are often observed to span across the different layers of connections, which makes a challanging task to extract the information encoded in their multi-layer community organization. With particular focus on the real social multiplex network of the APS Scientific Collaboration Multiplex Network we have tried to characterize both form a dynamical and from a structural point of view the organization of multi-layer communities in the attept of generalizing the concept of community to multilayer systems. References: 1) "Triadic closure as a basic generating mechanism of communities in complex networks", Ginestra Bianconi, Richard K. Darst, Jacopo Iacovacci, and Santo Fortunato Phys. Rev. E 90 (2014) 2) "Emergence of Multiplex Communities in Collaboration Networks", Battiston F, Iacovacci J, Nicosia V, Bianconi G, Latora V (2016)PLoS ONE 11(1)