Supporting strategic decision making in an enterprise university through detecting patterns of academic collaboration

Collaborative networks are a topic, broadly researched from several perspectives, including the social network analysis (SNA). The organisations take advantage from the results of SNA to determine collaborative channels, information fusion through such channels and key participants or groups in the network. This work is focused on multi-facet analysis of academic collaboration, as it has been identified as a key factor in success and growth in the global educational market. The data sets include integrated data about different aspects of academic collaboration, including co-authorship, co-participation, co-supervision and other related data. We explore the concept of interestingness and its application to the field of network mining. Composing an appropriate interpretable set of interestingness measures will benefit decision makers in organisations in taking specific actions depending on the patterns in these measures. In this study we focus on interesting measures such as unexpectedness for academic networks and a collaborative score.