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Social networks contain implicit knowledge that can be used to inferhierarchical relations that are not explicitly present in the available data.Interaction patterns are typically affected by users' social relations. Wepresent an approach to inferring such information that applies a link-analysisranking algorithm at different levels of time granularity. In addition, avoting scheme is employed for obtaining the hierarchical relations. Theapproach is evaluated on two datasets: the Enron email data set, where the goalis to infer manager-subordinate relationships, and the Co-author data set,where the goal is to infer PhD advisor-advisee relations. The experimentalresults indicate that the proposed approach outperforms more traditionalapproaches to inferring hierarchical relations from social networks.

Detecting Hierarchical Ties Using Link-Analysis Ranking at Different Levels of Time Granularity