Abstract
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.
Highlights
Research on link prediction for social networks has been actively pursued [1,2,3,4,5,6,7,8]
Link Prediction Techniques Among various link prediction techniques proposed in the literature, the common neighbor (CN) [9], Adamic/Adar (AA) [10], preferential attachment (PA) [9], Jaccard coefficient (JC) [27], and resource allocation (RA) [11] are widely used and their accuracies have been explored for several types of networks [3,12,13,28]
We briefly introduce definitions of the link prediction score li, j for the following types of link prediction techniques for unweighted and weighted networks: CN [9] and weighted CN (WCN) [4,28], AA [10] and weighted AA (WAA) [4,28], PA [9] and weighted PA (WPA) [28], JC [27] and weighted JC (WJC) [1], and RA [11] and weighted RA (WRA) [4]
Summary
Research on link prediction for social networks has been actively pursued [1,2,3,4,5,6,7,8]. Several link prediction techniques have been proposed [4,5,9,10,11]. These techniques can be used to predict new link formation by estimating the likelihood of link formation between two nodes on the basis of the observed network topology. Social ties can be defined in a number of ways, and the accuracy of link prediction techniques has been investigated for several types of social networks such as coauthorship networks [3], email networks [12], and friendship networks [13]. Link prediction techniques are expected to be utilized for several applications such as recommendation [3], anomaly detection [14], network modeling [15], missing link detection [6], evaluation of network evolution mechanisms [16], reconstruction of networks [17], and classification of partially labeled networks [18,19]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
