Abstract
Link prediction in social networks such as collaboration networks and friendship networks have recently attracted a great deal of attention. There have been numerous attempts to address this problem through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most of the previous studies neglected the impact of time stamps of the interactions and of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduced a new time aware index, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We apply time score to two social network data sets, namely, a coauthorship network data set and a Facebook friendship network data set. The results reveal a significant improvement in predicting future links.
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Munasinghe, L., Ichise, R. (2011). Time Aware Index for Link Prediction in Social Networks. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2011. Lecture Notes in Computer Science, vol 6862. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23544-3_26
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DOI: https://doi.org/10.1007/978-3-642-23544-3_26
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