Abstract. Recently, we started to experience a shift from physical communities to virtual communities, which leads to missed social opportunities in our daily routine. For instance, we are not aware of neighbors with common interests or nearby events. Mobile social computing applications (MSCAs) promise to improve social connectivity in physical communities by leveraging information about people, social relationships, and places. This article presents MobiSoC, a middleware that enables MSCA development and provides a common platform for capturing, managing, and sharing the social state of physical communities.Additionally, it incorporates algorithms that discover previously unknown emergent geo-social patterns to augment this state. To demonstrate MobiSoC's feasibility, we implemented and tested on smart phones two MSCAs for location-based mobile social matching and place-based ad hoc social collaboration. Experimental results showed that MobiSoC can provide good response time for 1000 users. We also demonstrated that an adaptive localization scheme and carefully chosen cryptographic methods can significantly reduce the resource consumption associated with the location engine and security on smart phones. A user study of the mobile social matching application proved that geo-social patterns can double the quality of social matches and that people are willing to share their location with MobiSoC in order to benefit from MSCAs.
Abstract-Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups. This information, known to group members but otherwise unavailable, can be leveraged in applications and protocols, such as recommender systems or delay-tolerant forwarding in ad hoc networks, to enhance the user experience. Group discovery using Bluetooth co-location is practical because: (i) Bluetooth is embedded in nearly every phone and has low battery consumption, (ii) the short wireless transmission range can lead to good group identification accuracy, and (iii) privacy-conscious users are more likely to share co-location data than absolute location data. This paper proposes the Group Discovery using Co-location traces (GDC) algorithm, which leverages user meeting frequency and duration to accurately detect groups. GDC is validated on one month of data collected from 141 smart phones carried by students on our campus. Users rated GDC's groups 30% better than groups discovered using the K-Clique algorithm. Additionally, GDC lends itself more easily to a distributed implementation, which achieves similar results with the centralized version while improving user's privacy.
Abstract-This paper explores how online social networks and co-presence social networks complement each other to form global, fused social relations. We collected Bluetoothbased co-presence data from mobile phones and Facebook social data from a shared set of 104 students. For improved analysis accuracy, we created weighted social graphs based on meeting frequency and duration for co-presence data, and based on wall writing and photo tagging for Facebook data. By analyzing the overall structural properties, we show the two networks represent two different levels of social engagement which complement each other. By fusing them together, the average path length and network diameter is shortened, and consequently the social connectivity increases significantly. By quantifying the contribution of each social network to the fused network in terms of node degree, edge weight, and community overlap, we discovered that the co-presence network improves social connectivity, while the online network brings greater cohesiveness to social communities.
Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups, which can enhance recommender systems or allow detection of epidemic events. Group discovery using Bluetooth co-location is practical due to low power consumption, short range, and applicability to decentralization. This paper presents the Group Discovery using Co-location traces (GDC) and Decentralized GDC (DGDC) algorithms, which leverage user meeting frequency and duration to accurately detect groups. GDC and DGDC are validated on one month of data collected from 141 smart phones carried by students on our campus, and by comparison against ground-truth groups.
Abstract-The widespread adoption of smart phones allows for the seamless capture of social interactions on a scale that was once impossible. Co-presence, collected using Bluetooth on the phones, faithfully represents such real-world social interactions. This social information can be transformed into communities, which can be leveraged into applications such as recommender systems and collaborative tools. However, correctly identifying communities is difficult.This paper presents TIE, a visualization tool that enables effective review of detected communities. With TIE, we can visualize the social interaction of a set of people over time. Also, TIE can overlay detected community events in a usable way over the underlying social interactions. Further, it allows us to investigate specific social interaction events and see how well detected communities match those events. Lastly, it enables the comparison of different sets of detected communities by interactively switching between overlays. TIE has proven useful in evaluating our community detection algorithms and has been invaluable in identifying strengths and weaknesses of these algorithms. Beyond our needs, TIE is usable for other data sets that can be reduced to temporal interaction events such as multiplayer game communities, SMS interactions, and paper co-authorship.
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