Examining Different Research Communities: Authorship Network

TL;DR

This paper analyzes authorship networks in Data Mining and Software Engineering using Google Scholar data from 2000-2021, revealing domain-specific network structures, influential authors, and community formations.

Contribution

It presents a comparative network analysis of two computer science domains, highlighting differences in community structures and influential authors over two decades.

Findings

Distinct network features for each domain

Identification of influential authors and organizations

Presence of small communities within networks

Abstract

Google Scholar is one of the top search engines to access research articles across multiple disciplines for scholarly literature. Google scholar advance search option gives the privilege to extract articles based on phrases, publishers name, authors name, time duration etc. In this work, we collected Google Scholar data (2000-2021) for two different research domains in computer science: Data Mining and Software Engineering. The scholar database resources are powerful for network analysis, data mining, and identify links between authors via authorship network. We examined coauthor-ship network for each domain and studied their network structure. Extensive experiments are performed to analyze publications trend and identifying influential authors and affiliated organizations for each domain. The network analysis shows that the networks features are distinct from one another and exhibit small communities within the influential authors of a particular domain.