Presenting a Dataset for Collaborator Recommending Systems in Academic Social Network: a Case Study on ReseachGate

TL;DR

This paper introduces two new structured datasets derived from ResearchGate to facilitate research in collaborator recommendation systems, including analysis and validation of their effectiveness in academic social networks.

Contribution

The paper provides the first publicly available, processed, and analyzed large-scale dataset from ResearchGate for developing and evaluating collaborator recommending systems.

Findings

The datasets enable potential collaborator identification.

Analysis shows the co-author relation best propagates knowledge.

The datasets are validated through multiple relation assessments.

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, which is the lack of the structured dataset to be used by the researchers. We introduce two datasets to fill this gap. The first dataset is prepared for designing a consistent, collaborator finding system. The next one, called a co-author finding model, models an academic social network as a table that contains different relations between the pair of users. Both of them provide 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 about users. In the preprocessing phase, the well-known Elmo was used for analyzing textual data. We call this as ResearchGate dataset for Recommending Systems (RGRS). For assessing the validity of data, we analyze each layer of data separately, and the results are reported. After preparing data and evaluating the collaborator finding models, we have done some assessments on RGRS. Some of these assessments are co-author, following-follower, and question answering relations. The outcomes indicate that it is the best relation in propagating knowledge in the network. To the best of our knowledge, there is no processed and analyzed dataset of this size.