• Medapati Lalitha

Network community detection is a fundamental task that finds applications in various fields. This research delves into the complexities of identifying network communities that transcend different domains. It specifically explores insights derived from the analysis of intricate networks in three unique areas: road traffic, biomedical studies, and the identification of game makers. In this study, we provide a comprehensive overview of the methods and techniques used for identifying network communities in these fields. We make use of actual data sources, including traffic flow data, biological interaction networks, and social interactions among game developers. The outcomes of our research unveil both similarities and distinct challenges in recognizing network communities within these domains. We employ cutting-edge algorithms and visualization tools to extract meaningful patterns, shedding light on the inherent modular structures within these networks. Furthermore, we examine the practical implications of our findings, showcasing how the detection of network communities can improve traffic management, deepen our understanding of disease pathways, and bolster the game development ecosystem. This research contributes to a broader comprehension of network structures that span diverse domains. It underscores the importance of tailored approaches to community detection in each unique context. The insights we've uncovered provide a solid foundation for future research directions and underscore the significance of interdisciplinary collaboration within the realm of network science.

Network Community Detection, Cross-Domain Analysis, Complex Networks, Road Traffic Networks, Biomedical Networks, Game Maker Identification Networks, Methodologies and Techniques, Data Analysis, Modular Structures, Commonalities and Challenges, Real-World Data Sources, Traffic Management, Disease Pathways, Game Development Ecosystem, Interdisciplinary Collaboration, Network Structures

"Cross-Domain Network Community Detection: Insights from Complex Road Traffic, Biomedical, and Game Maker Identification Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.i442-i448, March-2023, Available :http://www.jetir.org/papers/JETIR2303866.pdf

"Cross-Domain Network Community Detection: Insights from Complex Road Traffic, Biomedical, and Game Maker Identification Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppi442-i448, March-2023, Available at : http://www.jetir.org/papers/JETIR2303866.pdf