Abstract: Mobile dating applications (MDAs) have skyrocketed in popularity in the last few years. In addition to becoming an influential part of modern dating culture, MDAs facilitate a unique form of mediated communication: dyadic mobile text messages between pairs of users who are not already acquainted. Furthermore, mobile dating has paved the way for analysis of these digital interactions via massive sets of data generated by the instant matching and messaging functions of its many platforms at an unprecedented scale. This work looks at one of these sets of data: details from approximately two million conversations between heterosexual users on an MDA. These conversations consist of 19 million messages exchanged between 400,000 users. Through computational analysis methods, this study offers the very first large scale quantitative depiction of mobile dating as a whole. We report on differences in how heterosexual male and female users communicate with each other on MDAs, differences in behaviors of dyads of varying degrees of social separation, and factors leading to “success”—operationalized by phone number exchange. We find that there are fundamental differences male and female users regarding their communication patterns. We identify the key predictors of "success" among the information extracted from the messages' metadata. Finally we show hoe social separation between the matched users correlates with the likelihood of having a "successful" match.

Abstract: One of the key aspects behind the success of Internet is the use of optical fiber data transmission medium. In a single optical fiber, many different communication channels can transmit information simultaneously, using a different wavelength each. Today, the assignment of wavelengths to channels is fixed. However, research has shown that a flexible allocation could be more efficient; leading to much more data being transmitted using the same fiber. The flexible assignment of wavelengths is achieved by dividing the spectrum into small units, known as slots. Each communication channel is then assigned as many slots as necessary as long as the slots are contiguous in spectrum and exactly the same set of slots is used in every link travelled by the data. Most flexible wavelength allocation algorithms use a greedy approach: a new channel is established as long as there are enough contiguous slots to accommodate it. However, due to the dynamic of the network (channels being established and released), such approach could lead to spectrum fragmentation and inefficient usage of spectrum. A new approach, called Deadlock-Avoidance (DA), only establishes a new connection if the set of contiguous slots left after allocating it is big enough to accommodate future channels. Otherwise, the channel is not established even if there are available slots to allocate it. The behavior of DA has been evaluated for incremental traffic (channels are never released) in a single link scenario, showing a better performance than the greedy approach. The aim of this work is evaluating the dynamic behavior of DA in a more realistic scenario by using a Markov chain model (for the single link case) and event-driven simulation (for all the scenarios). Results shed light on the key aspects affecting the dynamic performance of flexible wavelength assignment algorithms.

Abstract: The communication and migration patterns of a country are shaped by its socioeconomic processes. The economy of Senegal is predominantly rural, as agriculture employs over 70% of the labor force. We have used a combination of mobile phone records and satellite images to explore the impact of agricultural activity on the communication and mobility patterns of the inhabitants of Senegal [1]. By means of the construction and analysis of time series and complex networks, we have found two peaks of phone calls activity emerging during the growing season. Moreover, during the harvest period, we detect an increase in the migration flows throughout the country. Another factor that shapes the communication and mobility patterns are traditional religious festivities, which are often held in a particular city. This implies the temporal migration of large masses of people, leaving a detectable trace recorded in the data that we explore with the aid of evolving temporal networks. Hence, in the light of our results, agricultural activity and religious holidays are the primary drivers of mobility inside the country. References [1] S. Martin-Gutierrez, J. Borondo, A. J. Morales, J. C. Losada, A. M. Tarquis and R. M. Benito, “Agricultural activity shapes the communication and migration patterns in Senegal”, Chaos: An Interdisciplinary Journal of Nonlinear Science, 2016, In press.

Abstract: Citation networks can be used to make powerful analyses about human intellectual activity in diverse fields. However, universal rules governing their structure and dynamics have not yet been discovered. To address this, my research probes the influence of social and institutional hierarchy on the structure and dynamics of citation networks. Hierarchy is a fundamental feature of all human social organizations; therefore, any citation network is necessarily embedded in an “underlying” hierarchy that in turn determines properties of the network. Through this new way of analyzing citation networks, my research seeks to advance the understanding of phenomena central to societal progress, such as: the emergence of research fronts and seminal publications; how paradigms form, take hold, become unstable, and collapse; innovation and the emergence of new technologies; and the emergence of new legal doctrine and the evolution of the law. I will present an analysis of a novel data set (that I have created) that covers all hierarchical levels of the Canadian legal system for a specific area of law (defamation law). My presentation will show: 1) an evaluation of a recently published method for inferring hierarchies among scientific journals based on scientific citation networks by applying that method to my data set, in order to determine if the method is capable of detecting the known underlying court hierarchy; and 2) ways in which network analysis methods (node-ranking via authority scores and node-grouping via community detection/clustering) can identify important periods in the evolution of the law (e.g. turning-points in legal “eras”, in which the law is applied in a new way). Points 1 and 2 will be discussed in relation to the overarching goal of understanding the influence of underlying hierarchy on the structure and evolution of citation networks in law and other fields.

Abstract: Understanding how people move within a geographic area, e.g. a city, a country or the whole world, is fundamental in several applications, from predicting the spatio-temporal evolution of an epidemics to inferring migration patterns. The possibility to gather information about the population through mobile phone data —recorded by mobile carriers triggered a wide variety of studies showing, for instance, that mobile phones heterogeneously penetrated both rural and urban communities, regardless of richness, age or gender, providing evidences that mobile technologies can be used to build realistic demographics and socio-economics maps of low-income countries, and also provide an excellent proxy of human mobility, showing for instance, that movements exhibit a high level of memory, i.e. the movements of the individuals are conditioned by their previous visited locations. However, the precise role of memory in widely adopted proxies of mobility, as mobile phone records, is unknown. We have used 560 millions of call detail records from Senegal to show that standard Markovian approaches, including higher-order ones, fail in capturing real mobility patterns and introduce spurious movements never observed in reality. We introduce an adaptive memory-driven approach to overcome such issues. At variance with Markovian models, it is able to realistically model conditional waiting times, i.e. the probability to stay in a specific area depending on individual's historical movements. Our results demonstrate that in standard mobility models the individuals tend to diffuse faster than what observed in reality, whereas the predictions of the adaptive memory approach significantly agree with observations. We show that, as a consequence, the incidence and the geographic spread of a disease could be inadequately estimated when standard approaches are used, with crucial implications on resources deployment and policy making during an epidemic outbreak.