Search

Even though the concept of tie strength is central in social network analysis, it is difficult to quantify how strong social ties are. One typical way of estimating tie strength in data-driven studies has been to simply count the total number or duration of contacts between two people. This, however, disregards many features that can be extracted from the rich data sets used for social network reconstruction. Here, we focus on contact data with temporal information. We systematically study how features of the contact time series are related to topological features usually associated with tie strength. We focus on a large mobile-phone dataset and measure a number of properties of the contact time series for each tie and use these to predict the so-called neighbourhood overlap, a feature related to strong ties in the sociological literature. We observe a strong relationship between temporal features and the neighbourhood overlap, with many features outperforming simple contact counts. Features that stand out include the number of days with calls, number of bursty cascades, typical times of contacts, and temporal stability. These are also seen to correlate with the overlap in diverse smaller communication datasets studied for reference. Taken together, our results suggest that such temporal features could be useful for inferring social network structure from communication data.

5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly—outage and sudden hype in traffic activity that may result in congestion—detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)’s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes $L$ -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)—notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector.

The availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.

Paper present new approach of urban multimodal mobility (UMM) estimation using anonymized data from public mobile network (PMN). The data set is derived from Call Data Records database, and urban multimodal mobility indicators were defined and relativized. Usage of indicators relativized values ensures that they can be applied for mobility estimation in all urban environment regardless of their physical differences, with existing public mobile network as single prerequisite. Travel mode classification is based on Adaptive Neuro Fuzzy Inference System (ANFIS) and trained using set of rules that were determined using method of surveying experts in domain of urban mobility. Accurate estimate creates a foundation for improvement of existing end creation of new services in urban mobility. Also, this approach has potential through implementation within advanced applications of Intelligent Transport Systems with the goal to improve travel modal shift, passenger comfort, efficiency of urban transport etc.

The advent of geolocated information and communication technologies opens the possibility of exploring how people use space in cities, bringing an important new tool for urban scientists and planners, especially for regions where data are scarce or not available. Here we apply a functional network approach to determine land use patterns from mobile phone records. The versatility of the method allows us to run a systematic comparison between Spanish cities of various sizes. The method detects four major land use types that correspond to different temporal patterns. The proportion of these types, their spatial organization and scaling show a strong similarity between all cities that breaks down at a very local scale, where land use mixing is specific to each urban area. Finally, we introduce a model inspired by Schelling's segregation, able to explain and reproduce these results with simple interaction rules between different land uses., Partial financial support was received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under project INTENSE@COSYP (FIS2012-30634), and from the EU Commission through projects LASAGNE and INSIGHT. The work of M.L. was funded under the PD/004/2013 project, from the Conselleria de Educación, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational programme for 2013–2017. J.J.R. acknowledges funding from the Ramón y Cajal program of MINECO.

© 2015, Springer Science+Business Media New York. Social network contacts have significant influence on individual travel behavior. However, transport models rarely consider social interaction. One of the reasons is the difficulty to properly model social influence based on the limited data available. Non-conventional, passively collected data sources, such as Twitter, Facebook or mobile phones, provide large amounts of data containing both social interaction and spatiotemporal information. The analysis of such data opens an opportunity to better understand the influence of social networks on travel behavior. The main objective of this paper is to examine the relationship between travel behavior and social networks using mobile phone data. A huge dataset containing billions of registers has been used for this study. The paper analyzes the nature of co-location events and frequent locations shared by social network contacts, aiming not only to provide understanding on why users share certain locations, but also to quantify the degree in which the different types of locations are shared. Locations have been classified as frequent (home, work and other) and non-frequent. A novel approach to identify co-location events based on the intersection of users’ mobility models has been proposed. Results show that other locations different from home and work are frequently associated to social interaction. Additionally, the importance of non-frequent locations in co-location events is shown. Finally, the potential application of the data analysis results to improve activity-based transport models and assess transport policies is discussed., The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007–2013 under grant agreement n° 318367 (EUNOIA project) and n° 611307 (INSIGHT project). The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educación, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013–2017.

Increasing use of cell phone as a medium of human interaction is playing a vital role in solving riddles of crime as well. A young girl went missing from her home late in the evening in the month of August, 2008 when her enraged relatives and villagers physically assaulted and chased her fiancée who often frequented her home. Two years later, her mother lodged a complaint against the relatives and the villagers alleging that after abduction her daughter was either sold or killed as she had failed to trace her. On investigation, a rusted cell phone with partial visible IMEI number, clothes, bangles, human skeleton etc. recovered from abandoned well in the month of May, 2011 were examined in the lab. All hopes pinned on identity of cell phone, for only linking evidence to fix the scene of occurrence supported by call detail record (CDR) and to dispel doubts about mode of sudden disappearance or death as DNA technology did not help in establishing identity of the deceased. The conventional scientific methods were used without success and international mobile equipment identification number of the cell phone could be generated by using statistical analysis followed by online verification., {"references":["","http://en.wikipedia.org/wiki/Mobile_phone.","Thilagaraj R. and Kala N. An approach to finalizing the technical\ninvestigation of cyber crime within law enforcement, The Indian police\nJournal, vol. LX, Jan-March 2013 no.1, pp. 90-103.","Carlton G.H. and Worthley R., An evaluation of agreement & conflict\namong computer forensic experts, Proceedings of the 42nd Hawaii\nInternational Conference on system Science. (2009).","AIR 2005SC3820, 2005 CriLJ3950, 122 (2005) DLT194 (SC), (2005)\n11SCC600, (2005) Suppl (3) SCR79.","Crl. A. No. 41/2008 (Decided on March 18, 2009).","Crl. A. No. 561/2008 (Decided on March 16, 2009).","http://www.asianage.com/delhi/man-gets-20-yr-jail-killing-ddagardener-\n867 (Decided in February 2009).","http://en.wikipedia.org/wiki/Scott_peterson.","http://en.weikipedia.org/wiki/Luhn_algorithm.\n[10] http://expressbuzz.com/finance/mobile-phone-import-not-chinaspecific/\n172492.html."]}

Information spreading simulation is an important problem in scientific community and is widely studied nowadays using different techniques. Efficient users’ activity simulation for urgent scenarios is even more important, because fast and accurate reaction in such situations can save human lives. In this paper we present multi-layer agent-based network model for information spreading simulation in urgent scenarios, which allows to investigate agents’ behavior in a variety of situations. This model can be used for live city simulation in integration with other agent-based human interaction models. Experimental results demonstrate logical consistency of the proposed approach and show different cases of information spreading in the network with different social aspect.