The Experts below are selected from a list of 1992 Experts worldwide ranked by ideXlab platform
Vittoria Colizza - One of the best experts on this subject based on the ideXlab platform.
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Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models
Royal Society Open Science, 2017Co-Authors: Cecilia Panigutti, Michele Tizzoni, Zbigniew Smoreda, Pietro Bajardi, Vittoria ColizzaAbstract:The recent availability of large-scale Call Detail Record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematiCally compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.
Jan Vanthienen - One of the best experts on this subject based on the ideXlab platform.
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Social network analytics for churn prediction in telco: Model building, evaluation and network architecture
Expert Systems with Applications, 2017Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct Call-Detail Record datasets, originating from telecommunication organizations across the world. We statistiCally evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way, ranging from network architecture to model building and evaluation.
Cecilia Panigutti - One of the best experts on this subject based on the ideXlab platform.
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Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models
Royal Society Open Science, 2017Co-Authors: Cecilia Panigutti, Michele Tizzoni, Zbigniew Smoreda, Pietro Bajardi, Vittoria ColizzaAbstract:The recent availability of large-scale Call Detail Record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematiCally compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.
Benxiong Huang - One of the best experts on this subject based on the ideXlab platform.
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identifying hot lines of urban spatial structure using cellphone Call Detail Record data
Ubiquitous Intelligence and Computing, 2014Co-Authors: Shu Chen, Benxiong HuangAbstract:The rapid growth of cell phone users in cities enable the cell phone towers spread all over urban area in past years. The user Call logs, which refer to users movement trajectory in urban area, can provide an opportunity to understand urban spatial structure. As the extraction of more popular channel of human movement in urban area, the hot lines highlighted the spatial morphology of human flows in urban structure. In this paper, we propose popularity index that utilizes diversity and density index of channel to identify the hot lines based on cell phone Call Detail Record dataset. The density of cell phone users that travel across one channel and the diversity of travel behaviors from different cell phone users refer to one channel has been combined to infer the level of popularity index for each channel. In the case study, a Call Detail Record dataset that generated from the users of an anonymous telecom in Wuhan has been applied to identify the hot lines. The results showed the effectiveness of our approach and can be used as references for more explicitly representing urban dynamics to support urban plan applications.
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UIC/ATC/ScalCom - Identifying Hot Lines of Urban Spatial Structure Using Cellphone Call Detail Record Data
2014 IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing and 2014 IEEE 11th Intl Conf on Autonomic and Trusted Computing and 2014 IEEE 14th I, 2014Co-Authors: Shu Chen, Benxiong HuangAbstract:The rapid growth of cell phone users in cities enable the cell phone towers spread all over urban area in past years. The user Call logs, which refer to users movement trajectory in urban area, can provide an opportunity to understand urban spatial structure. As the extraction of more popular channel of human movement in urban area, the hot lines highlighted the spatial morphology of human flows in urban structure. In this paper, we propose popularity index that utilizes diversity and density index of channel to identify the hot lines based on cell phone Call Detail Record dataset. The density of cell phone users that travel across one channel and the diversity of travel behaviors from different cell phone users refer to one channel has been combined to infer the level of popularity index for each channel. In the case study, a Call Detail Record dataset that generated from the users of an anonymous telecom in Wuhan has been applied to identify the hot lines. The results showed the effectiveness of our approach and can be used as references for more explicitly representing urban dynamics to support urban plan applications.
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Activity recognition from Call Detail Record: Relation between mobile behavior pattern and social attribute using hierarchical conditional random fields
Proceedings - 2010 IEEE ACM International Conference on Green Computing and Communications GreenCom 2010 2010 IEEE ACM International Conference on Cyb, 2010Co-Authors: Chen Zhou, Benxiong HuangAbstract:Mobile phone, as a kind of most commonly used vehicle of communication, keep Records of every movements of each person. For each cell phone user, the different social attribute, leading to various mobility behaviors and social cliques, reflect on dissimilarity of their Call behavior patterns. How to deduce the social attribute from the Calling behavior is discussed in this paper, by estimated the time he spent on his business, his family or his friends. The data contains 749 users 3 months Call Detail Records (CDR) with the 5 different jobs, which is selected randomly from database of a telecommunication operator who refuse to apprize its name. In this paper, the daily behavior of one user is divided into 48 parts with every half an hour as a basic element which is labeled with one activity-mode. There are eight activity-modes, inferred using hierarchical conditional random fields (HCRF), including four work-purpose states, two chat-purpose states and two other states as 3 basic elements of Calling behavior. The cluster result is shown and the analyses of relation between the cluster and the job are made.
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GreenCom/CPSCom - Activity Recognition from Call Detail Record: Relation Between Mobile Behavior Pattern and Social Attribute Using Hierarchical Conditional Random Fields
2010 IEEE ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber Physical and Social Computing, 2010Co-Authors: Chen Zhou, Benxiong HuangAbstract:Mobile phone, as a kind of most commonly used vehicle of communication, keep Records of every movements of each person. For each cell phone user, the different social attribute, leading to various mobility behaviors and social cliques, reflect on dissimilarity of their Call behavior patterns. How to deduce the social attribute from the Calling behavior is discussed in this paper, by estimated the time he spent on his business, his family or his friends. The data contains 749 users 3 months Call Detail Records (CDR) with the 5 different jobs, which is selected randomly from database of a telecommunication operator who refuse to apprize its name. In this paper, the daily behavior of one user is divided into 48 parts with every half an hour as a basic element which is labeled with one activity-mode. There are eight activity-modes, inferred using hierarchical conditional random fields (HCRF), including four work-purpose states, two chat-purpose states and two other states as 3 basic elements of Calling behavior. The cluster result is shown and the analyses of relation between the cluster and the job are made.
Nitesh V. Chawla - One of the best experts on this subject based on the ideXlab platform.
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Inferring Unusual Crowd Events from Mobile Phone Call Detail Records
2016Co-Authors: Yuxiao Dong, Zubair Nabi, Fabio Pinelli, Yiannis Gkoufas, Nitesh V. ChawlaAbstract:Abstract. The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behavior in urban environments. Cities can leverage such knowledge to provide better services (e.g., public transport planning, optimized resource allo-cation) and safer environment. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we propose a methodology that is able to detect unusual events from CDR data, which typiCally has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16 % higher reCall and over 10 × higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparse-ness and distinction between user unusual activities and daily routines.
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ECML/PKDD (2) - Inferring unusual crowd events from mobile phone Call Detail Records
Machine Learning and Knowledge Discovery in Databases, 2015Co-Authors: Yuxiao Dong, Zubair Nabi, Fabio Pinelli, Francesco Calabrese, Yiannis Gkoufas, Nitesh V. ChawlaAbstract:The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behavior in urban environments. Cities can leverage such knowledge to provide better services e.g., public transport planning, optimized resource allocation and safer environment. Call Detail Record CDR data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we propose a methodology that is able to detect unusual events from CDR data, which typiCally has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher reCall and over 10$$\times $$ higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.
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Inferring unusual crowd events from mobile phone Call Detail Records
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015Co-Authors: Yuxiao Dong, Zubair Nabi, Fabio Pinelli, Francesco Calabrese, Yiannis Gkoufas, Nitesh V. ChawlaAbstract:The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typiCally has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher reCall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.
