Presenting a dataset for collaborator recommending systems in academic social network: a case study on ReseachGate.

Zahra Roozbahani, Jalal Rezaeenour, Hanif Emamgholizadeh, Amir Jalali Bidgoly.

Abstract: Collaborator finding systems are a special type of expert finding models. There is a long lasting challenge for research in the collaborator recommending research area, that is the lake of structured dataset to be used by the researchers. We introduce two datasets to fill this gap. The first dataset models an academic social network as a directed multi-relation network. The next one models an academic social network as a table which contains different relations between the pair of users. The last model provides an opportunity for introducing potential collaborators to each other. These two models have been extracted from ResearchGate (RG) data set and are available publicly. RG dataset has been collected from Jan. 2019 to April 2019 and includes raw data of 3980 RG users. The dataset consists of almost complete information of users. In the preprocessing phase the well-known Elmo was used for analyzing textual data. We call this set of data Multirelation ResearchGate Nnetwork (MRGN). For assessing the validity of data, we analyze each layer of data separately, and the results are reported.

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