Leveraging Bluetooth co-location traces in group discovery algorithms

Boston, Daniel; Mardenfeld, Steve; Pan, Juan; Jones, Quentin; Iamnitchi, Adriana; Borcea, Cristian

doi:10.1016/j.pmcj.2012.10.003

Cited by 7 publications

(6 citation statements)

References 37 publications

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“…Graph clustering has been used in a lot of research to find social groups in which a network is separated into disjoint communities using clustering techniques [40]. Several studies on community detection have been published based on network members' contact histories, such as encounter frequency and length, and on a person's total number of previous contacts [41], [42]. Authors in [41] proposed group discovery using co-location (GDC) and decentralized GDC (DGDC) methods, which use the frequency and length of meetings to reliably discover groups.…”

Section: Social Correlation Detectionmentioning

confidence: 99%

“…Several studies on community detection have been published based on network members' contact histories, such as encounter frequency and length, and on a person's total number of previous contacts [41], [42]. Authors in [41] proposed group discovery using co-location (GDC) and decentralized GDC (DGDC) methods, which use the frequency and length of meetings to reliably discover groups. In [42], the authors used models that can reliably assess, predict, and cluster multi-modal data from people and groups within a population's social network to reveal the structure inherent in everyday activities.…”

Section: Social Correlation Detectionmentioning

confidence: 99%

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Companion Mobility to Assist in Future Human Location Prediction

et al. 2022

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Location prediction plays an important role in modeling human mobility. Existing studies focused on developing a prediction model which is based solely on the past mobility of only the person of interest (POI), rather than including information on the mobility of her/his companions. In fact, people frequently move in a group, and thus, using mobility data of a person's companions can enhance accuracy when predicting that person's future locations. Motivated by this, we propose a two-phase framework for predicting an individual's future locations that fully benefits from spatio-temporal contexts embedded in that person's and his/her companions' mobility. The framework first determines the POI's companions, then predicts future locations based on mobility information for both the POI and selected companions. Two companion selection methods are proposed in this work. The first method uses spatial closeness (SC) to determine the companions of the POI by measuring the similarity of the individuals' geographic distributions. The second method builds person ID embedding (PIE) vectors, and cosine similarity is used to select the POI's companions. To mitigate the curse of dimensionality, the framework also uses a stacked autoencoder in which the encoder compresses a high-dimensional input feature (e.g., location, time, and person ID) into a low-dimensional latent vector. For the second phase of the framework, a bidirectional recurrent neural network (BRNN)-based multi-output model is proposed to predict a person's future locations in the next several time slots. To train the BRNN model, weighted loss is used, which takes into account the importance of each future time slot to predict the POI's locations accurately. Experiments are conducted on two largescale Wi-Fi trace datasets, demonstrating that the proposed model can effectively predict human future locations.

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Section: Social Correlation Detectionmentioning

confidence: 99%

Section: Social Correlation Detectionmentioning

confidence: 99%

Companion Mobility to Assist in Future Human Location Prediction

et al. 2022

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“…A number of studies were conducted to detect social communities using graph clustering [24,37] where a network was divided into disjoint communities by using clustering techniques. There were several studies on community detection based on the contact history of members in the network, e.g., encounter frequency and duration [38] and the total number of past encounters of a person [39]. Eagle and Pentland represented the behavior of individuals from a set of primary vectors called eigenbehaviors [39].…”

Section: Social Community Detectionmentioning

confidence: 99%

Predicting Human Location Using Correlated Movements

Dao

Yoon

2019

Electronics

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This paper aims at estimating the current location, or predicting the next location, of a person when the recent location sequence of that person is unknown. Inspired by the fact that the behavior of an individual is greatly related to other people, a two-phase framework is proposed, which first finds persons who have highly correlated movements with a person-of-interest, then estimates the person’s location based on the position information for selected persons. For the first phase, we propose two methods: community interaction similarity-based (CISB) and behavioral similarity-based (BSB). The CISB method finds persons who have similar encounters with other members in the entire community. In the BSB method, members are selected if they show similar behavioral patterns with a given person, even though there are no direct encounters or evident co-locations between them. For the second phase, a neural network is considered in order to develop the prediction model based on the selected members. Evaluation results show that the proposed prediction model under the BSB scheme outperforms other methods, achieving top-1 accuracy of 71.13% and 69.36% for estimations of current and next locations, respectively, with the MIT dataset and 92.31% and 92.03% in case of the Dartmouth dataset.

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“…In [20], Boston et al proposed an algorithm for detecting social groups based on Bluetooth traces. They employed the frequency and duration of users' meetings in order to group users.…”

Section: Social Structure Detectionmentioning

confidence: 99%

Discovering Social Community Structures Based on Human Mobility Traces

Nguyen

Yoon

Kim

2017

Mobile Information Systems

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We consider a community detection problem in a social network. A social network is composed of smaller communities; that is, a society can be partitioned into different social groups in which the members of the same group maintain stronger and denser social connections than individuals from different groups. In other words, people in the same community have substantially interdependent social characteristics, indicating that the community structure may facilitate understanding human interactions as well as individual’s behaviors. We detect the social groups within a network of mobile users by analyzing the Bluetooth-based encounter history from a real-life mobility dataset. Our community detection methodology focuses on designing similarity measurements that can reflect the degree of social connections between users by considering tempospatial aspects of human interactions, followed by clustering algorithms. We also present two evaluation methods for the proposed schemes. The first method relies on the natural properties of friendship, where the longevity, frequency, and regularity characteristics of human encounters are considered. The second is a movement-prediction-based method which is used to verify the social ties between users. The evaluation results show that the proposed schemes can achieve high performance in detecting the social community structure.

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Leveraging Bluetooth co-location traces in group discovery algorithms

Cited by 7 publications

References 37 publications

Companion Mobility to Assist in Future Human Location Prediction

Companion Mobility to Assist in Future Human Location Prediction

Predicting Human Location Using Correlated Movements

Discovering Social Community Structures Based on Human Mobility Traces

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