Information and Communication Technologies

Study of the structure and evolution of citation networks has potential to advance understanding of human activity in fields such as science, law, and patenting [1-3]. A main area of research in this vein has been the application of community detection (clustering) methods to citation networks [4]. While these methods are capable of grouping the nodes of a citation network according to known areas of science (e.g. physics, chemistry, mathematics, etc.) [5] or law (e.g.

The explosion of location-based services allowed for a new range of technological application influencing our every day life in several contexts including health, sport, transportation, entertainment, work, and personal life. However, it also represents a new source of concerns regarding personal or sensitive information shared by the user and/or stored on the platform, which cannot be simply overcome by data anonymisation [1].

The governance of large tourist flows attracted by the historical cities is becoming a fundamental issue for the preservation of cultural heritage and the
life quality of residences. The Venice case study paradigmatic since the major has recently proposed limitations in the number of entrances in the historical center. Moreover the continuous increasing of the tourist flows corresponds a to a parallel decreasing of the number of residences changing Venice is a 'museum city' with large desert areas.
The main problems are:

Recent social and political events, such as the 2016 US presidential election,
have been marked by a growing number of so-called ``fake news'',
i.e. fabricated information that
disseminate deceptive content, or grossly distort actual news reports, shared on
social media platforms.
While misinformation and propaganda have existed since ancient times,
their importance and influence
in the age of social media is still not clear.

We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Our method consists of four parts: (i)variable selection, (ii) grouping, (iii) round robin, and (iv) regression analysis.

Despite the robust structure of the Internet, it is still susceptible to misconfigurations and/or intentional updates that prevent network traffic from being routed to its intended destination. In this work we study three such large-scale disruptions and analyze how they impact the Internet topology. We use distributed views to generate temporal graphs of the routing dynamics of the Internet at the Autonomous System (AS) level. We analyzed the topological properties of reconstructed AS-level graphs before, during, and after these incidents.

Stochastic processes are a keen interest of study across most scientific disciplines. Manifest in economic markets, biological evolution, the weather, crystal formation, and numerous other phenomena, they are ubiquitous in nature but often hard to simulate and predict.

Graph Semi-Supervised Learning (gSSL) is a classification paradigm that has received a great amount of attention due to its ability to exploit the structure of unlabeled data toghether with expert data in order to develop classifiers. Moreover, gSSL endorses an interpretation in terms of random walks propagating labeled data through the graph structure, which is the mechanism fuelling its force [1].

It is now generally accepted that politics has become more fast-moving and unstable, which has led to election surprises and even regime changes. While modern politics seems more volatile, there is little systematic evidence to support this. In this project, we seek to address this gap with data reported over the last fifty years using public opinion polls and media coverage data in the UK and Germany. These countries are good cases to study because both have experienced considerable changes in electoral behaviour and new political parties during the time period studied.