GroupMe: Supporting Group Formation with Mobile Sensing and Social Graph Mining

Guo, Bin; He, Huilei; Yu, Zhiwen; Zhang, Daqing; Zhou, Xingshe

doi:10.1007/978-3-642-40238-8_17

Cited by 16 publications

(20 citation statements)

References 11 publications

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“…For future work, we will focus on building a friendship recommendation model with incremental update to achieve the scalability. We will also investigate the usage of our friend prediction algorithm for social activity organization [34] and opportunistic social networking [35]. The prior one can suggest potential participants to be invited by activity initiators, whereas the latter one can detect nearby like-minded people and cue potential interactions among them.…”

Section: Discussionmentioning

confidence: 99%

Friendship Prediction Based on the Fusion of Topology and Geographical Features in LBSN

Luo

Guo

Zhiwenyu³

et al. 2013

2013 IEEE 10th International Conference on High Performance Computing and Communications &Amp; 2013 IEEE International Conferen

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Friendship prediction in social networks is useful for various applications, such as friend/place recommendation and privacy management. In this paper, we propose a friendship prediction approach by fusing the topology and geographical features in location based social networks (LBSNs). We investigate the features of users' relationship both online and offline and quantify the contributions of selected features through information gain metric. Three key features are selected, namely user social topology, location category, and check-in location. Friendship is predicted based on the fusion of the selected online/offline features. Three inference models are selected to infer the friendship, including Random Forests, Support Vector Machine (SVM), and Naive Bayes. The proposed approach is validated by intensive empirical evaluations using the collected Foursquare and Jiepang datasets.

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Section: Discussionmentioning

confidence: 99%

Friendship Prediction Based on the Fusion of Topology and Geographical Features in LBSN

Luo

Guo

Zhiwenyu³

et al. 2013

2013 IEEE 10th International Conference on High Performance Computing and Communications &Amp; 2013 IEEE International Conferen

Self Cite

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“…The idea behind human grouping, for purpose of designing effective incentive mechanisms, is that participants in each group will share similar social and economic goals, different from other groups and hence would be motivated differently. The importance of incorporating human grouping into mobile crowdsensing, a process described by Lane [122] as community-aware sensing, has been emphasised in several studies, including [123,124]. GroupMe, proposed in [124] is a giant stride in this direction to help facilitate the discovery of groups within mobile crowdsensing systems.…”

Section: Research Opportunities and Challengesmentioning

confidence: 99%

“…The importance of incorporating human grouping into mobile crowdsensing, a process described by Lane [122] as community-aware sensing, has been emphasised in several studies, including [123,124]. GroupMe, proposed in [124] is a giant stride in this direction to help facilitate the discovery of groups within mobile crowdsensing systems. However, several research challenges still need to be adequately addressed in order to fully maximise the potential of using human grouping to improve the outcomes of decision making related to appropriateness of incentive mechanism.…”

Section: Research Opportunities and Challengesmentioning

confidence: 99%

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Ogie

2016

Hum. Cent. Comput. Inf. Sci.

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Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.

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“…In general, group behavior is recognized based on interaction [21], similarity [22], or proximity [1] [2]. However, all these approaches are centralized.…”

Section: Related Workmentioning

confidence: 99%

Grace: Recognition of Proximity-Based Intentional Groups Using Collaborative Mobile Devices

Han

2014

2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems

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People in social environments often appear in groups to have face-to-face interactions, automatically recognizing these groups will facilitate many applications while being unobtrusive. In this work, we focus on recognizing multiple concurrent intentional groups of people in proximity using a real-time distributed approach running on commodity mobile devices. Specifically, we study Bluetooth signal strength probability distribution for proximity estimation and model the group probability distribution under Bluetooth signal strength. Further, we develop a realtime distributed group determination algorithm. We implement a prototype system on iOS platforms which includes both the group recognition service and a mobile social application using the service. Both our simulation and prototype results show that the proposed approach can recognize proximity-based intentional groups with high accuracy.

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GroupMe: Supporting Group Formation with Mobile Sensing and Social Graph Mining

Cited by 16 publications

References 11 publications

Friendship Prediction Based on the Fusion of Topology and Geographical Features in LBSN

Friendship Prediction Based on the Fusion of Topology and Geographical Features in LBSN

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Grace: Recognition of Proximity-Based Intentional Groups Using Collaborative Mobile Devices

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