An Experimental Analysis to Learn Data Imbalance in Scholarly Data: A Case Study on ResearchGate

Abstract

Data imbalance is a key challenge in the majority of real-world classification problems. It refers to the disparity of data instances corresponding to either of the class labels. Data imbalance is studied in detail with respect to many data domains such as transaction data, medical data, e-commerce data, meteorological data, social media data, and web data. But the scholarly data domain is yet to be analyzed pertaining to data imbalance. In this chapter, the scholarly data domain is explored with a focus to study various forms of data imbalance. A well-known and popular scholarly platform, ResearchGate (RG), is targeted to extract real scholarly data. An extensive experimental analysis is performed on the extracted data in order to identify the existence of both data-level and network-level imbalance. The outcome contributes to the learning of various types of data imbalance that exist in scholarly data. Resolving the existing data imbalance will substantially help in achieving efficient and accurate outcomes in many real-world scholarly literature applications.