Mathematical network models applied to the analysis of mobile applications behavior

[EN] The network topologies are present in different social, political, economic and technological phenomena. These network structures allow to share information, alliances generation, behavior influence, opinion spread and virus transmission, among other aspects. Online networks are a reflection of the offline world and they also show these kind of network structures, in such a way that they allow the information transmission, social circle or community detection, affinity prediction between individuals, generation of recommendations, detection of influence people and generation of viral phenomena. Although all of these networks exhibit heterogeneity, they have enough underlying structure to allow their modelization for the study and analysis of all the listed phenomena. Nowadays, the line between the offline world and the online world is becoming more diffuse and there are network structures where both natures are mixed: There are almost as many mobile phones as individuals and in developed societies, the pervasiveness of smartphones on day-to-day is unquestionable in such a way that almost everybody is almost always connected everywhere. This permanent connection means that the individual, simultaneously and in a continuous mode, is a node belonging to its social network and its social network online. A key aspect of smartphones are the mobile applications that can be downloaded to the device. There are many applications for a host of different uses and the user behavior with these applications is the factor that determines how these applications behave. Also, mobile applications are the main source of infection of viruses on smartphones and, in this case, also the user behavior is what determines the transmission of these viruses. That is, the number of downloads of the application, the retention time of the application without being uninstalled, weekly minutes of usage, the popularity of the application, the transmission of viruses between smartphones, etc., depend on user behavior and, since the user is part of a social "offline" network and a social online network, in which the information is shared, communities are generated, behavior is influenced, opinion is spread and viruses are transmitted, we can intuit that the application behaviors can be modeled considering the network structure which user belongs to, so it is possible to analyze and study issues such as predicting the retention and download of applications and/or the transmission of viruses between smartphones. The purpose of this thesis is to analyze the behavior of mobile applications through mathematical network models. The behavior of mobile applications will be defined by the network of the users, taking into account parameters such as user behavior and technical issues of the mobile devices, so for model the networks both factors will be taken into account.[ES] Las estructuras de redes están presentes en multitud de fenómenos sociales, políticos, económicos y tecnológicos. Estas estructuras permiten compartir información, constituir alianzas, influir en comportamientos, generar corrientes de opinión, y transmitir virus, entre otros aspectos. Las redes online son un reflejo del mundo "analógico" y también presentan este tipo de estructura de red, de tal forma que permiten transmitir información, detectar comunidades, predecir afinidades entre individuos, generar recomendaciones, identificar individuos influyentes o producir fenómenos virales. Aunque todas estas redes son de naturaleza heterogénea, la estructura subyacente que presentan permiten su modelización para el estudio y análisis de los fenómenos indicados. Actualmente, la línea que divide el mundo "analógico" y el mundo online es cada vez más difusa produciéndose estructuras de redes donde se entremezclan ambas naturalezas: Existen casi tantos teléfonos móviles como individuos y, en las sociedades desarrolladas, la omnipresencia de los smartphones en el día día es incuestionable de tal forma que cualquier persona está conectada casi en todo momento y lugar. Esta conexión permanente conlleva que el individuo constituya simultáneamente y de un modo continuo un nodo de su estructura de red social y de su red social online. Una parte fundamental de los smartphones son las aplicaciones que se pueden descargar en el dispositivo. Existen multitud de aplicaciones para infinidad de utilidades distintas y el comportamiento del usuario frente a esas aplicaciones es el que determina cómo se comportan dichas aplicaciones. Asimismo, las aplicaciones móviles son la principal fuente de contagio de virus en los smartphones y en este caso, también el comportamiento del usuario es el que determina la transmisión de esos virus. Es decir, el número de descargas de la aplicación, el tiempo de retención de la aplicación sin ser desinstalada, los minutos semanales de uso, la popularidad de la aplicación, la transmisión de virus en smartphones, etc., dependen del comportamiento del usuario y, puesto que el usuario forma parte de una red social "offline" y una red social online, en las cuales se comparte y transmite información, se constituyen comunidades, se influye en los comportamientos, se generan corrientes de opinión y se transmiten virus, podemos intuir que los comportamientos de las aplicaciones pueden ser modelizados considerando la estructura de red de la que el usuario forma parte, de tal forma que sea posible analizar y estudiar aspectos tales como predecir la descarga y retención de aplicaciones y/o la transmisión de virus entre smartphones. El propósito de la presente tesis doctoral es modelizar y analizar el comportamiento de las aplicaciones móviles mediante estructuras de red. El comportamiento de las aplicaciones móviles vendrá definido por la red formada por los usuarios, teniendo en cuenta tanto parámetros de comportamiento de los usuarios como parámetros relacionados con aspectos técnicos de los dispositivos móviles, por lo que para la modelización de las redes se tendrán en cuenta ambos factores.[CA] Les estructures de xarxes estàn presents en multitud de fenòmens socials, pol'itics, econòmics i tecnològics. Estes estructures permeten compartir informació, constituir aliances, influir en comportaments, generar corrents d'opinió, i transmetre virus, entre altres aspectes. Les xarxes online són un reflex del món analògic i també presenten este tipus d'estructura de xarxa, de tal forma que permet transmetre informació, detectar comunitats, predir afinitats entre individus, generar recomanacions, identificar individus influents o produir fenòmens virals. Encara que totes estes xarxes són de naturalesa heterogènia, l'estructura subjacent que presenten permeten la seua modelització per a l'estudi i anàlisi dels fenòmens indicats. Actualment, la línia que dividix el món analògic i el món online és cada vegada més difusa produintse estructures de xarxes on s'entremesclen ambós naturaleses: Existixen quasi tants telèfons mòbils com individus i, en les societats desenvolupades, l'omnipresència dels smartphones en el dia a dia és inqüestionable de tal forma que qualsevol persona està connectada quasi en tot moment i lloc. Esta connexió permanent comporta que l'individu constituïsca simultàniament i d'una manera contínua un node de la seua estructura de xarxa social i de la seua xarxa social online. Una part fonamental dels smartphones són les aplicacions que es poden descarregar en el dispositiu. Hi ha multitud d'aplicacions per a infinitat d'utilitats distintes i el comportament de l'usuari enfront d'eixes aplicacions és el que determina com es comporten aquestes aplicacions. Així mateix, les aplicacions mòbils són la principal font de contagi de virus en els smartphones i en este cas, també el comportament de l'usuari és el que determina la transmissió d'eixos virus. És a dir, el nombre de descàrregues de l'aplicació, el temps de retenció de l'aplicació sense ser esborrada, els minuts setmanals d'ús, la popularitat de l'aplicació, la transmissió de virus entre smartphones, etc., depenen del comportament de l'usuari i, ja que l'usuari forma part d'una xarxa social "offline" i una xarxa social online, en les quals es compartix i es transmet informació, es constituïxen comunitats, s'influïx en els comportaments, es generen corrents d'opinió i es transmeten virus, podem intuir que els comportaments de les aplicacions poden ser modelitzats considerant l'estructura de xarxa de què l'usuari forma part, de tal forma que siga possible analitzar i estudiar aspectes com ara predir la descàrrega i retenció d'aplicacions i/o la transmissió de virus entre smartphones. El propòsit de la present tesi doctoral és modelitzar i analitzar el comportament de les aplicacions mòbils per mitjà d'estructures de xarxa. El comportament de les aplicacions mòbils vindrà definit per la xarxa formada pels usuaris, tenint en compte tant paràmetres de comportament dels usuaris com paràmetres relacionats amb aspectes tècnics dels dispositius mòbils, per la qual cosa per a la modelització de les xarxes es tindràn en compte ambdós factors.Alegre Sanahuja, J. (2016). Mathematical network models applied to the analysis of mobile applications behavior [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67389TESI

Predicting mobile apps spread: An epidemiological random network modeling approach

[EN] The mobile applications business is a really big market, growing constantly. In app marketing, a key issue is to predict future app installations. The influence of the peers seems to be very relevant when downloading apps. Therefore, the study of the evolution of mobile apps spread may be approached using a proper network model that considers the influence of peers. Influence of peers and other social contagions have been successfully described using models of epidemiological type. Hence, in this paper we propose an epidemiological random network model with realistic parameters to predict the evolution of downloads of apps. With this model, we are able to predict the behavior of an app in the market in the short term looking at its evolution in the early days of its launch. The numerical results provided by the proposed network are compared with data from real apps. This comparison shows that predictions improve as the model is fed back. Marketing researchers and strategy business managers can benefit from the proposed model since it can be helpful to predict app behavior over the time anticipating the spread of an appAlegre-Sanahuja, J.; Cortés, J.; Villanueva Micó, RJ.; Santonja, F. (2017). Predicting mobile apps spread: An epidemiological random network modeling approach. Transactions of the Society for Computer Simulation. 94(2):123-130. https://doi.org/10.1177/0037549717712600S12313094

Agent-Based Model to Study and Quantify the Evolution Dynamics of Android Malware Infection

[EN] In the last years the number of malware Apps that the users download to their devices has risen. In this paper, we propose an agentbased model to quantify the Android malware infection evolution, modeling the behavior of the users and the different markets where the users may download Apps. The model predicts the number of infected smartphones depending on the type of malware. Additionally, we will estimate the cost that the users should afford when the malware is in their devices. We will be able to analyze which part is more critical: the users, giving indiscriminate permissions to the Apps or not protecting their devices with antivirus software, or the Android platform, due to the vulnerabilities of the Android devices that permit their rooted. We focus on the community of Valencia, Spain, although the obtained results can be extrapolated to other places where the number of Android smartphones remains fairly stable.This work has been partially supported by the Ministerio de Econom´ıa y Competitividad Grant MTM2013-41765-P.Alegre Sanahuja, J.; Camacho Vidal, FJ.; Cortés López, JC.; Santonja, F.; Villanueva Micó, RJ. (2014). Agent-Based Model to Study and Quantify the Evolution Dynamics of Android Malware Infection. Abstract and Applied Analysis. 2014:1-10. https://doi.org/10.1155/2014/623436S1102014Di Cerbo, F., Girardello, A., Michahelles, F., & Voronkova, S. (2011). Detection of Malicious Applications on Android OS. Lecture Notes in Computer Science, 138-149. doi:10.1007/978-3-642-19376-7_12Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., & Weiss, Y. (2011). «Andromaly»: a behavioral malware detection framework for android devices. Journal of Intelligent Information Systems, 38(1), 161-190. doi:10.1007/s10844-010-0148-xBose, A., & Shin, K. G. (2011). Agent-based modeling of malware dynamics in heterogeneous environments. Security and Communication Networks, 6(12), 1576-1589. doi:10.1002/sec.298Wang, P., Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A.-L. (2009). Understanding the Spreading Patterns of Mobile Phone Viruses. Science, 324(5930), 1071-1076. doi:10.1126/science.1167053Mylonas, A., Kastania, A., & Gritzalis, D. (2013). Delegate the smartphone user? Security awareness in smartphone platforms. Computers & Security, 34, 47-66. doi:10.1016/j.cose.2012.11.004Hoare, A., Regan, D. G., & Wilson, D. P. (2008). Sampling and sensitivity analyses tools (SaSAT) for computational modelling. Theoretical Biology and Medical Modelling, 5(1), 4. doi:10.1186/1742-4682-5-

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)