2016 Contribution to conference Open Access

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2016 Report Open Access

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Analisi e profilazione dei comportamenti di videogiocatori online con tecniche di Data Mining
Grano C.
Il lavoro svolto verte sull'analisi dei comportamenti degli utenti-giocatori di un gioco mobile di un'azienda Spagnola (From The Bench SL). Le analisi sono state condotte con l'ausilio di Hadoop e Python grazie ai quali si è effettuata la profilazione degli utenti. L'obiettivo del lavoro è stato quello di studiare i comportamenti degli utenti per generare e migliorare le azioni di fidelizzazione.Project(s): SoBigData

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2016 Journal article Open Access

Homophilic network decomposition: a community-centric analysis of online social services
Rossetti G., Pappalardo L., Kikas R., Pedreschi D., Giannotti F., Dumas M.
In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks--Skype, LastFM and Google+--and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.Source: Social Network Analysis and Mining 6 (2016). doi:10.1007/s13278-016-0411-4
DOI: 10.1007/s13278-016-0411-4
Project(s): CIMPLEX , SoBigData
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See at: Social Network Analysis and Mining | link.springer.com | Social Network Analysis and Mining | ISTI Repository | CNR ExploRA

A novel approach to evaluate community detection algorithms on ground truth
Rossetti G., Pappalardo L., Rinzivillo S.
Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.Source: Complex Networks VII. Proceedings of the 7th Workshop on Complex Networks, pp. 133–144, Dijon, France, 23-25 March 2016
DOI: 10.1007/978-3-319-30569-1_10
Project(s): CIMPLEX , SoBigData
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See at: Studies in Computational Intelligence | link.springer.com | CNR ExploRA

The Haka network: Evaluating rugby team performance with dynamic graph analysis
Cintia P., Pappalardo L., Coscia M.
Real world events are intrinsically dynamic and analytic techniques have to take into account this dynamism. This aspect is particularly important on complex network analysis when relations are channels for interaction events between actors. Sensing technologies open the possibility of doing so for sport networks, enabling the analysis of team performance in a standard environment and rules. Useful applications are directly related for improving playing quality, but can also shed light on all forms of team efforts that are relevant for work teams, large firms with coordination and collaboration issues and, as a consequence, economic development. In this paper, we consider dynamics over networks representing the interaction between rugby players during a match. We build a pass network and we introduce the concept of disruption network, building a multilayer structure. We perform both a global and a micro-level analysis on game sequences. When deploying our dynamic graph analysis framework on data from 18 rugby matches, we discover that structural features that make networks resilient to disruptions are a good predictor of a team's performance, both at the global and at the local level. Using our features, we are able to predict the outcome of the match with a precision comparable to state of the art bookmaking.Source: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1095–1102, San Francisco, Ca, USA, 18-21 August 2016
DOI: 10.1109/asonam.2016.7752377
Project(s): SoBigData
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See at: doi.org | ieeexplore.ieee.org | CNR ExploRA

2016 Journal article Open Access

An analytical framework to nowcast well-being using mobile phone data
Pappalardo L., Vanhoof M., Gabrielli L., Smoreda Z., Pedreschi D., Giannotti F.
An intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory.Source: International Journal of Data Science and Analytics (Print) 2 (2016): 75–92. doi:10.1007/s41060-016-0013-2
DOI: 10.1007/s41060-016-0013-2
DOI: 10.48550/arxiv.1606.06279
Project(s): CIMPLEX , PETRA , SoBigData
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See at: arXiv.org e-Print Archive | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | ISTI Repository | doi.org | link.springer.com | CNR ExploRA

2016 Conference article Open Access

Human mobility modelling: exploration and preferential return meet the gravity model
Pappalardo L., Rinzivillo S., Simini F.
Modeling the properties of individual human mobility is a challenging task that has received increasing attention in the last decade. Since mobility is a complex system, when modeling individual human mobility one should take into account that human movements at a collective level influence, and are influenced by, human movement at an individual level. In this paper we propose the d-EPR model, which exploits collective information and the gravity model to drive the movements of an individual and the exploration of new places on the mobility space. We implement our model to simulate the mobility of thousands synthetic individuals, and compare the synthetic movements with real trajectories of mobile phone users and synthetic trajectories produced by a prominent individual mobility model. We show that the distributions of global mobility measures computed on the trajectories produced by the d-EPR model are much closer to empirical data, highlighting the importance of considering collective information when simulating individual human mobility.Source: 7th International Conference on Ambient Systems, Networks and Technologies, ANT 2016; 6th International Conference on Sustainable Energy Information Technology, SEIT 2016;, pp. 934–939, Madrid (ES), 23-26 Maggio 2016
DOI: 10.1016/j.procs.2016.04.188
Project(s): CIMPLEX , PETRA , SoBigData
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See at: Procedia Computer Science | Explore Bristol Research | www.sciencedirect.com | CNR ExploRA

2016 Report Open Access

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Assessing Privacy Risk & Quality of Spatio-temporal Data
Pellungrini R.
Mobility data are a fundamental source of information for studying human behavior and developing new services for users. In recent years, with the growing presence of smartphones in our lives and the increasing connectivity of devices used everyday, data regarding the whereabouts of individuals in time has become more and more available. However, the exchange and publication of such data may lead to dangerous privacy violations for the people involved. Malicious third parties may try and succeed in identifying individuals even in a deidentified dataset, by attacking in various ways the published data. In this work, we propose a study of the safeness of a common data framework for trajectories, in order to understand the levels of risk for the people involved. We will introduce and develop a simulation of a number of different types of attack and we will apply them to a real mobility dataset. We will then try to understand if such levels of risk can impair the safe use of the data themselves. We will also evaluate how the quality of the most used mobility measures changes by eliminating data of individuals with high privacy risk.Project(s): SoBigData

See at: etd.adm.unipi.it | CNR ExploRA

SoccerAtlas: a web framework for visual soccer analytics
Kenny J.
This thesis discusses the design and development of SoccerAtlas, a web framework for visual soccer analytics. During the past decade data science have entered the world of sports and large amounts of hi-fidelity soccer data are now readily available, collected by several companies through advanced semi-automatic sensing technologies. However soccer analytics is a young field of research, and the wealth of the collected data is not yet actually supported by adequate analysis tools. This is particularly true for soccer, which among all the team sports is the most difficult to quantitatively analyse due to the complexity of the play and to the low number of scores which determine the result of the game. SoccerAtlas is a web application that allows the soccer analyst to visually explore soccer data, interact with it, and gain new insights. It exploits several analytical functions to extract useful information from data, then visualising it in an appropriate way. This thesis discusses all the steps that lead to the final web framework, taking into account both technological aspects and visual communication aspects. Particularly, these steps include the definition of a theoretical framework which illustrates the abstract model of the data, the formalization of the analytical functions based on this model, the design and the implementation of the framework. Some examples of analysis performed on a real soccer game data are also provided in order to show the potential of the analytical tool. The resulting web framework is built using several technologies, such as Python, HTML, CSS and d3.js, the JavaScript library which is nowadays the standard for web-based data visualization projects.Project(s): SoBigData

See at: etd.adm.unipi.it | CNR ExploRA