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

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

Similarity Index based Link Prediction Algorithms in Social Networks: A Survey, Journal of Telecommunications and Information Technology, 2016, nr 2

Social networking sites have gained much popularity in the recent years. With millions of people connected virtually generate loads of data to be analyzed to infer meaningful associations among links. Link prediction algorithm is one such problem, wherein existing nodes, links and their attributes are analyzed to predict the possibility of potential links, which are likely to happen over a period of time. In this survey, the local structure based link prediction algorithms existing in literature with their features and also the possibility of future research directions is reported and discussed. This survey serves as a starting point for beginners interested in understanding link prediction or similarity index algorithms in general and local structure based link prediction algorithms in particular