Examining Different Research Communities: Authorship Network

📅 2024-08-24
🏛️ arXiv.org
📈 Citations: 0
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🤖 AI Summary
This study investigates structural and evolutionary differences in author collaboration networks between data mining and software engineering, aiming to uncover community organization, temporal dynamics, and distributions of influential scholars and institutions. Method: Leveraging Google Scholar data from 2000–2021, we construct and comparatively analyze the two networks using graph-theoretic metrics—including sparsity, clustering coefficient, small-worldness, modularity, and degree centrality—alongside temporal modeling and Louvain community detection. Contribution/Results: We empirically demonstrate that both fields exhibit highly clustered, localized small-world communities; however, their influence distributions diverge markedly: data mining displays a hub-and-spoke pattern dominated by a few highly connected authors, whereas software engineering manifests a multi-centric, diffused structure. The analysis identifies core author clusters and high-output institutions in each domain, offering quantitative evidence to inform interdisciplinary collaboration strategies and disciplinary development assessment.

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📝 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.
Problem

Research questions and friction points this paper is trying to address.

Analyzing co-authorship networks in Data Mining and Software Engineering
Identifying influential authors and organizations within research communities
Examining publication trends and network structures across disciplines
Innovation

Methods, ideas, or system contributions that make the work stand out.

Google Scholar data extraction
Co-authorship network analysis
Influential author identification
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