Quasi-realtime social network construction with heterogeneous sensors in ambient environment

Tsugawa, Sho; Ohsaki, Hiroyuki; Itoh, Yoshio; Ono, Naoaki; Kagawa, Keiichiro; Takashima, Kazuki; Imase, Makoto

doi:10.1145/2071423.2071515

Cited by 1 publication

(1 citation statement)

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“…Recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people [20] – [23] . For instance, in the SocioPatterns project, social networks representing face-to-face interactions among participants at an academic conference are constructed using badge-shaped sensing devices [20] .…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Link Prediction for Face-to-Face Behavioral Networks

Tsugawa

Ohsaki

2013

PLoS ONE

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Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30–0.45 and a recall of 0.10–0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks.

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Section: Introductionmentioning

confidence: 99%