Penggunaan metode community detection pada graf
Abstract
This research addresses the use of community detection methods in the analysis of complex network structures. Community detection is an important technique for identifying groups of nodes that are more closely connected within a network, compared to their connections with other nodes outside the group. This understanding is useful in fields such as social networks, biology, and cybersecurity, where community detection helps uncover hidden patterns and relationships in the data. Several major community detection methods, such as modularity-based, hierarchical, partitioning, density, and spectral algorithms, are analyzed in this study. Based on the experiments conducted, each method has its advantages and disadvantages in detecting communities on certain types of networks. This research also covers the applications of community detection in various fields and provides suggestions for further research in the development of more efficient community detection methods that can be applied to dynamic networks.
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References
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