Abstract: Real world applications that use Unmanned Aerial Vehicles (UAVs or drones) are now a reality. They are used in situations known as Dull, Dangerous and Dirty (DDD). The next step is the adoption of swarms, i.e. of a number of UAVs that collaborate to achieve a mission. We focus on autonomous collaborative drones, i.e. drones that take decisions without any control from the outside. Autonomy is mandatory because in large swarms one cannot technically afford neither to have each UAV connected to the ground nor to have one ground pilot per UAV. Swarms offer a number of advantages, among which are continuous flight, resilience support, complementarity of sensing capacities. Combining UAVs gives rise to features that would otherwise be unfeasible. For example, it is possible to analyse the quality of air over a city by flying the UAVs of a swarm at different altitudes; this cannot be achieved with a single UAV. In terms of advantages, to paraphrase Aristotle, a swarm of drones is more than the sum of the drones that make it up (“The whole is more than the sum of its parts” Aristotle, Metaphysics). Of course, if a swarm brings more in terms of benefits it also brings more in terms of issues. Among these are communication, authentication, compact flight, safety. For instance, if considering the fault tree analysis of a swarm, the difficulty is the combinatorial explosion of the tree due to events that do not exist when one single UAV is considered, such as the possibility that one of the UAVs crashes into another in case of emergency landing. In terms of issues, as was the case regarding advantages, a swarm of drones is more than the sum of the drones that make it up. Swarms of autonomous drones are complex systems by nature.

Abstract: The literature has extensive claims about a causal relationship between growth in ICT investment and economic growth and the value it brings to businesses, education, and health. Previous studies had argued that with ICT diffusion, there is economic growth, and others argued that growth is conditional and partial. There is a lack of literature that explicitly states how ICT investments contributes to economic development or other impact. This is probably because measuring the impact of ICT is a challenge and a complex problem because there are a number of different ICTs, with different impacts in different contexts and countries. In addition, there is a web of relationships between impact areas and with the broader economy, society and environment. The aim of this complexity study is to gain insights on the value of ICT contributions to development by examining the interaction between different dimensions such as socio-economic growth, education, health, and the environment using agent based modeling (ABM). This paper argues for building models to understand emergence created by this complex environment in order to see if we are building better world through ICT investment and to direct investments in resources and efforts in the “right” place. ABM is a useful tool because it can effectively provide us with an experimentation environment that can answer complex questions. ABM is to be used to study individual and collective behavioral changes in using ICT in its different forms while interacting with other agents, such as aspects of the economy, health, education and the environment. The outcome of these models will provide in depth understanding of the emergence between multiple agents interacting with ICT at the micro-macro levels. The purpose of this paper is to mainly establish a foundation and interest for further research in using ABM to better understand ICT contribution to Development.

Abstract: User generated contents (UGCs) have gained immense popularity for exploring different socio-economic issues. However, considering all UGCs uniformly might be problematic. For example, UGCs can be intentional (such as microblogging sites) as well as unintentional (such as searching pattern). The intriguing question is - how these two types of UGCs are interrelated to each other? In this paper, we explored the similarities and differences between intentional and unintentional UGCs in the context of 2014 Ebola outbreak. Prior studies on the epidemic, mostly analyzed the entire UGC corpus or the time series data as a whole. This can be misleading in our context since there might exist a time-lag between unintentional and intentional UGCs. So, based on the anomalies in our time series data, we considered various subsamples (for a shorter period of time) for our analysis. Data were retrieved from Google (for online search pattern), Twitter (a real-time broadcasting channel for the epidemic) and Wikipedia (largest UGC for first-hand information) for our study. Wiki Trends data were collected for detecting anomalies (important events) which confirm WHO notifications. Google data were extracted around these events, to explore the topics that cropped up through searching. We also crawled the tweet feeds to probe the discussion on the Twitter platform during the same time period. We applied Latent Dirichlet Allocation(LDA) to probe underlying topics in the microblogging discussion. In addition to this temporal analysis, we also performed a spatial analysis by comparing geotagged tweets and locational information of Google Trends. Broadly our study indicates a similar pattern between intentional and unintentional UGCs. So, it is possible to identify and trace areas of concern, both in terms of spatial and temporal dimensions, during an epidemic by exploring UGCs. This approach can hence be useful for health organizations to tackle an epidemic.

Abstract: The microblogging platform Twitter has received much attention from researchers in the recent years. Many researchers have sought for an explanation as to why certain topics become trending and other do not. Usually, these studies focus on predictive aspects of the topic itself, rather than on the growth of the topic. In previous work, we employed a random graph model to mimic the growth of a topic and get a better understanding of how a topic can become trending, for which we found that the size of the Largest Connected Component (LCC) is a good indicator. Using a dataset containing a year of Dutch tweets scraped from Twitter using its streaming API, we analyze the retweet graphs corresponding to all hashtags used by more than hundred users in that year. We find that the corresponding retweet graphs tend to either have one LCC or are scattered in many small components. We then compare these outcomes with the estimates of the random graph model parameters.

Abstract: Every year, influenza epidemics affect millions of people and place a strong burden on health care services. A timely knowledge of the onset of the epidemic could allow these services to prepare for the peak. We will present a machine-learning based method that can reliably identify and signal the influenza outbreak. By combining official Influenza-Like Illness (ILI) incidence rates, searches for ILI-related terms on Google, and an on-call triage phone service, Saúde 24, we were able to identify the beginning of the flu season in 8 European countries, anticipating current official alerts by several weeks. This work shows that it is possible to detect and consistently anticipate the onset of the flu season, in real-time, regardless of the amplitude of the epidemic, with obvious advantages for health care authorities. We also show that the method is not limited to one country, specific region or language, and that it provides a simple and reliable signal that can be used in early detection of other seasonal diseases.

Abstract: Humans are social animals that love to disseminate ideas and news, as proved by the huge success of social networking websites such as Facebook or Twitter. On the other hand, these platforms have emphasized the dark side of information spreading, that is the diffusion of private facts and rumors in the society. Usually users of these social networks can set a level of privacy, and decide to whom to show their private facts, but they cannot control how their friends will use this information: they could spread it through other social websites, medias or simply with face-to-face communication. The classic Susceptible-Infectious-Recovered (SIR) epidemic model can be adopted for modeling the spread of information in a social network: susceptible individuals do not know the information, then are susceptible to be informed; infectious individuals know and spread the information, while recovered individuals already know the information but do not spread it anymore. A susceptible individual in contact with an infectious one can become infectious with a transmission probability, while an infectious individual naturally recovers from infection with a recovery rate, turning into a recovered individual. We extend this compartmental model in order to represent several kinds of privacy policies, from unsafer to more rigorous: each individual belongs to a class that models the privacy behavior by tuning the transmission probability, the recovery rate and the susceptibility to information, that specifies the interest of the individual on the information. We calculate a privacy score for each individual based on the privacy policies of her neighbors, so as to infer the local robustness to the spread of personal information. We test our model by means of stochastic simulations on synthetic contact networks and on a small partition of the Facebook social network, provided by few hundreds of volunteers that replied to an online survey.

Abstract: Digital data is transitioning from data in search of method, to method in search of theory. But, despite a wealth of clearly pertaining theory in the social sciences, it has proven exceedingly difficult to accomplish a fruitful connection: there appears to be an epistemological incompatibility between the computational approaches applied within digital data study and traditional social scientific research. This paper makes two observations about the epistemological nature of digital data. First, digital data is by nature relational and focused on the interaction between individuals, rather than on their individual attributes. This not only permits the study of emergence through computational models - but requires it. Second, digital systems are “arenas of interaction” where the emergence of social practices intermingles with technological change in the platforms that support said social practices. We’re looking at innovation, quite simply, only substantially faster. We interpret this situation through the lens of what Lane and Maxfield (2006) call "ontological uncertainty," which implies limits to the applicability of formal modeling (Andersson et al. 2014.) We find that digital data typically resides at a difficult epistemological crossroad between "mass-dynamics," which is amenable to computational modeling, and ontological uncertainty, which limits the applicability most types of modeling. This paper suggests a complexity epistemology of digital data that is question-driven and methodologically pluralist, using computational modeling tools to explore emergence, and the opportunities of vast new data sets, but does so within an epistemological framing that enables insights that are situated and reflexive.

Abstract: Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by being repeatedly and persistently among the first to collect the items that later become hugely popular. Since these users effectively discover future hits, we refer them as discoverers. We use the proposed framework to demonstrate that discoverers are present in a wide range of real systems. Discoverers are typically not among the most central nodes in the user-user social network, which indicates that users' ability to early collect future hits is essentially unrelated to users' social importance. We show that due to their ability to early identify future hits, discoverers can be used to predict the future success of new items based on the first few received links. Finally, we propose a network model which reproduces the discovery patterns observed in the real data. Data produced by the model are shown to pose a fundamental challenge to classical ranking algorithms on networks which neglect the time of link creation and thus fail to discriminate between discoverers and ordinary users in the data. Our results bring new insights into the quantitative characterization of users' behavior in online systems, and have far-reaching implications for trend prediction and algorithm design.

Abstract: Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.

Abstract: Being able to infer the number of people in a specific area is of extreme importance for the avoidance of crowd disasters and to facilitate emergency evacuations. Here, using a football stadium and an airport as case studies, we present evidence of a strong relationship between the number of people in restricted areas and activity recorded by mobile phone providers and the online service Twitter. Our findings suggest that data generated through our interactions with mobile phone networks and the Internet may allow us to gain valuable measurements of the current state of society. This presentation will allow me to further disseminate my work to an audience with broad interests. It will help me establish a personal network of connections in the complex systems scientific community. This may enable collaborations, which would be of great benefit in my career as a young researchers. Attendees of this talk can expect to see the importance of new forms of data both from a scientific point of view, but also their importance for policy makers and stakeholders. The presentation will be readily accessible to a broad audience, thus maximising the dissemination of the research. Reference: Botta F, Moat HS, Preis T. Quantifying crowd size with mobile phone and Twitter data. Royal Society Open Science 2, 150162 (2015)

Abstract: Big Data has become the primary source of understanding the structure and dynamics of the society at large scale. The network of social interactions can be considered as a multiplex, where each layer corresponds to one communication channel and the aggregate of all them constitutes the entire social network. However, usually one has information only about one of the channels, which should be considered as a sample of the whole. Here we show by simulations and analytical methods that this sampling may lead to bias. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get with reasonable assumptions about the sampling process a monotonously decreasing distribution as observed in empirical studies of single channel data. Also we find, that assortativity may occur or get strengthened due to the sampling process. We analyse the far-reaching consequences of our findings.

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.