Using Location-Based Social Networks to Validate Human Mobility and Relationships Models

Nguyen, Tommy; Szymański, Bolesław K.

doi:10.1109/asonam.2012.210

Cited by 29 publications

(18 citation statements)

References 14 publications

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“…The study of the dynamics of human mobility using the collected geolocations of users, and applying it to predict future users' locations, has been an active field of research [8] [9]. In particular, this information can be used to validate human mobility models (as the authors of [10] did with the information from a location-based social networking site); and to study the interplay between individual mobility and social networks [11].…”

Section: Introductionmentioning

confidence: 99%

Human mobility and predictability enriched by social phenomena information

Ponieman¹,

Salles²,

Sarraute³

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-The massive amounts of geolocation data collected from mobile phone records has sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, focusing in particular in the cases of urban commute and major sports events. We illustrate how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user's location.

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Section: Introductionmentioning

confidence: 99%

Human mobility and predictability enriched by social phenomena information

Ponieman¹,

Salles²,

Sarraute³

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…By using a location of interest based system, we avoid the need for the routers to know exact positions of the nodes that interact with them. Locations of interest in our cases are routers at notable locations, this is similar to the idea of locations labeled with a checkin name in the Gowalla system studied by Nguyen et al [14].…”

Section: Data Access Is Also An Important Issue In Delay Tolerantmentioning

confidence: 59%

“…In contrast, in our work we still enforce an idea of affinities, but they are developed non-deterministically based on previous decisions of the node and its community. Nguyen et al [14] present the important finding that there is a relationship between friendship and distance. This is important to our work because it means that given users in a community (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…However, the study done by Song et al does not provide insight about social communities. Using location based social media, authors in [14] showed that mobility of members of the same community is highly correlated. Nguyen et al developed a Markov model for each user based on their Gowalla checkins, which is used in their Friendship Mobility Model (FMM).…”

Section: Related Workmentioning

confidence: 99%

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Impact of socially based demand on the efficiency of caching strategy

Holzbauer

Szymanski

Bulut

2014

2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS)

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Abstract-Routers are often faced with a variety of users throughout a day. These users make requests, of which the most bandwidth consuming and least delay tolerant are requests for internet content including videos. To save cost of uploading all requested data, routers use content-aware caching schemes. The efficiency of such schemes is dependent on router ability to predict the use of the currently requested content. At the same time, research on social networks indicates that users with social ties have the correlated mobility patterns and the correlated interests in requested content. In this paper, we demonstrate that these correlations between mobility and between interests can be used to improve efficiency of content-based caching network caching by taking into account social ties between users visiting a router. To this end, we built a model for router visits and user demand based on social relations. We then used this model to create a content-aware caching scheme called "SOcial Content Caching Scheme (SOCCS)". Our results demonstrate that by using information about frequency of visit by members of different social communities combined with topic tags on the content requested by each community, the performance of routers can be increased remarkably compared to their performance with random caching. Moreover, such socially based caching is robust to changes in many parameters of our model that affect visits by mobile nodes, the tags of content that they request, and how history is incorporated into caching decisions.

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“…Motivated by the investigation of privacy concerns surrounding real CDRs, Mir et al extend WHERE to DP‐WHERE with a differentially private approach for modeling CDR‐based human mobility. Nguyen and Szymanski propose the Friendship Mobility Model (FMM) that generates empirical Markov Models using LBSN check‐ins. The FMM was validated for the MAC 802.11 back off timer algorithm against the RWP and Erdos‐Renyi (ER) models.…”

Section: Background and Related Workmentioning

confidence: 99%

CGAM: A community and geography aware mobility model

Mourchid

Kobbane

Othman

et al. 2017

Int J Communication

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SummaryThe advances of localization-enabled technologies have led to huge volumes of large-scale human mobility data collected from Call Data Records (CDR), Global Positioning System (GPS) tracking systems, and Location Based Social networks (LBSN). These location data that encompass mobility patterns could generate an important value for building accurate and realistic mobility models and hence important value for fields of application including context-aware advertising, city-wide sensing applications, urban planning, and more. In this paper, we investigate the underlying spatio-temporal and structural properties for human mobility patterns, and propose the Community and Geography Aware Mobility (CGAM) model, which characterizes user mobility knowledge through several properties such as home location distribution, community members' distribution, and radius of gyration. We validate the CGAM synthetic traces against real-world GPS traces and against the traces generated by the baseline mobility model SMOOTH and assess that CGAM is accurate in predicting the performance of flooding-based and community-based routing protocols. KEYWORDShuman mobility, mobility model, user patterns | INTRODUCTIONCharacterizing human mobility for different (daily or longtime) scales has received substantial attention, with the goal of designing mobility models that can express accurately several fundamental properties revealed in real traces of dynamic networks. The analysis, validation, and prediction of the performance of these mobility models could be fundamental for many fields of application ranging from context-aware advertising to epidemics prevention.Mobility models are classified into synthetic and trace-based mobility models. 1 The synthetic traces generated by a synthetic mobility model are far to be realistic, while those generated by a trace-based mobility model are expected to reproduce the statistical features present in human mobility. Therefore, the research community is more interested in the development and the use of mobility models that are based on real traces collected from several indoor and outdoor sites. 2 Moreover, many studies reported the discovery of fundamental statistical properties of human mobility such as flight length, flight duration, pause-times, and inter-contact times, extracted from real traces collected from bank notes tracking, Global Positioning System (GPS), CDR, Bluetooth encounters, and Location Based Social networks (LBSN) traces. 2 In this paper, we present a new mobility model, called Community and Geography Aware Mobility (CGAM) that is able to reproduce statistical properties of human mobility, to express geography, individual and community behavior features and thus simulating straightforwardly real-life scenarios. The CGAM model is proposed to combine the

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Using Location-Based Social Networks to Validate Human Mobility and Relationships Models

Cited by 29 publications

References 14 publications

Human mobility and predictability enriched by social phenomena information

Human mobility and predictability enriched by social phenomena information

Impact of socially based demand on the efficiency of caching strategy

CGAM: A community and geography aware mobility model

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