Leveraging Knowledge Graphs for Analyzing Open-Source Software Ecosystems: A GitHub Case Study

In this paper, we investigate the transformative potential of knowledge graphs which is also known as semantic network in analyzing and comprehending the complex relationships within the GitHub platform. GitHub, as a thriving hub for open-source development, fosters collaboration, knowledge sharing, and collective problem-solving among a diverse community of developers. Our research presents a comprehensive approach to harnessing Neo4j, a robust graph database, to represent GitHub data as a knowledge graph. This approach is used to further uncover profound relationships between repositories, contributors, and their activities, offering a holistic view of the open-source development landscape. Additionally, we delve into the social networking dynamics of GitHub, revolutionizing developer communication, collaboration, and professional growth. Our three-phased methodology covers data extraction and pre-processing, graph processing, and knowledge graph creation. The combination of these phases enhances our analytical capabilities and affords a thorough understanding of GitHub’s expansive ecosystem. Through visualization tools and graph algorithms, we identify influential contributors and highly contributed repositories to reveal interesting insights and valuable information.