Pendekatan Graph-Based Community Detection dalam Social Network Analysis Jaringan Undang-Undang Republik Indonesia 2014-2024

Setyawan Wibisono; Eko Nur Wahyudi; Wiwien Hadikurniawati; Endang Lestariningsih; Taufik Dwi Cahyono

doi:10.35315/dinamik.v30i2.10218

Setyawan Wibisono Universitas Stikubank
Eko Nur Wahyudi Universitas Stikubank
Wiwien Hadikurniawati Universitas Stikubank
Endang Lestariningsih Universitas Stikubank
Taufik Dwi Cahyono Universitas Semarang

DOI: https://doi.org/10.35315/dinamik.v30i2.10218

Abstract

This study evaluates the performance of three community detection algorithms—Leiden, Infomap, and Label Propagation—on the legal network of the Republic of Indonesia spanning the period 2014–2024. The network consists of 679 nodes and 2,295 edges, constructed based on citation relationships among regulations. The evaluation employs four network topology metrics: modularity, coverage, conductance, and inter-cluster density. Results show that the Leiden algorithm achieves the highest modularity score (0.522991), indicating the formation of communities with strong internal density. Additionally, it yields the lowest conductance value (0.302455), suggesting relatively well-isolated communities. In contrast, the Label Propagation algorithm produces the highest coverage (0.835294) and inter-cluster density (0.542331), but with a lower modularity (0.431583), reflecting the formation of large communities with less distinct boundaries. Infomap exhibits moderate performance, with a modularity score of 0.508406 and inter-cluster density of 0.420803, yet records a relatively high conductance (0.410409). Network visualizations reveal three major communities for each algorithm, representing thematic clusters such as institutional governance, constitutional law, and public finance. Overall, the Leiden algorithm is considered the most optimal for detecting modular, stable, and thematically coherent community structures within the complex and interrelated network of Indonesian laws.

References

[1] S. S. Singh, V. Srivastava, A. Kumar, S. Tiwari, D. Singh, dan H.-N. Lee, “Social Network Analysis: A Survey on Measure, Structure, Language Information Analysis, Privacy, and Applications,” ACM Trans. Asian Low-Resource Lang. Inf. Process., 2022, doi: 10.1145/3539732.
[2] R. Johnson, Eric M.; Chew, “Social Network Analysis for International Development,” Complex Adapt. Syst. Dev. Prof. Dev. Program., no. May, 2015.
[3] A. R. Anugerah, P. S. Muttaqin, dan W. Trinarningsih, “Social network analysis in business and management research: A bibliometric analysis of the research trend and performance from 2001 to 2020,” Heliyon, vol. 8, no. 4, hal. e09270, 2022, doi: 10.1016/j.heliyon.2022.e09270.
[4] M. Rosvall dan C. T. Bergstrom, “Maps of random walks on complex networks reveal community structure,” Proc. Natl. Acad. Sci. U. S. A., vol. 105, no. 4, hal. 1118–1123, 2008, doi: 10.1073/pnas.0706851105.
[5] C. Farage, D. Edler, A. Eklöf, M. Rosvall, dan S. Pilosof, “Identifying flow modules in ecological networks using Infomap,” Methods Ecol. Evol., vol. 12, no. 5, hal. 778–786, 2021, doi: 10.1111/2041-210X.13569.
[6] A. Holmgren, D. Edler, dan M. Rosvall, “Mapping change in higher-order networks with multilevel and overlapping communities,” Appl. Netw. Sci., vol. 8, no. 1, 2023, doi: 10.1007/s41109-023-00572-5.
[7] J. Smiljanić, C. Blöcker, D. Edler, dan M. Rosvall, “Mapping flows on weighted and directed networks with incomplete observations,” J. Complex Networks, vol. 9, no. 6, hal. 1–17, 2021, doi: 10.1093/comnet/cnab044.
[8] U. N. Raghavan, R. Albert, dan S. Kumara, “Near linear time algorithm to detect community structures in large-scale networks,” Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys., vol. 76, no. 3, hal. 1–11, 2007, doi: 10.1103/PhysRevE.76.036106.
[9] M. Ning, J. Gong, dan Z. Zhou, “A Novel Label Propagation Method for Community Detection Based on Game Theory,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 5, hal. 924–938, 2023, doi: 10.14569/IJACSA.2023.0140597.
[10] M. Yan dan C. Guoqiang, “Label Propagation Community Detection Algorithm Based on Density Peak Optimization,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/6523363.
[11] P. Z. Li, L. Huang, C. D. Wang, J. H. Lai, dan D. Huang, “Community detection by motif-aware label propagation,” ACM Trans. Knowl. Discov. Data, vol. 14, no. 2, 2020, doi: 10.1145/3378537.
[12] V. A. Traag, L. Waltman, dan N. J. van Eck, “From Louvain to Leiden: guaranteeing well-connected communities,” Sci. Rep., vol. 9, no. 1, hal. 1–12, 2019, doi: 10.1038/s41598-019-41695-z.
[13] R. Álvarez-Ugarte, “Social Network Analysis and the Law: A Proof of Concept for Argentina,” SSRN Electron. J., hal. 1–36, 2020, doi: 10.2139/ssrn.3711556.
[14] M. E. J. Newman dan M. Girvan, “Finding and evaluating community structure in networks,” Phys. Rev. E, vol. 69, no. 2, hal. 1–15, 2004.
[15] B. W. Kernighan dan S. Lin, “An Efficient Heuristic Procedure for Partitioning Graphs,” Bell Syst. Tech. J., vol. 49, no. 2, hal. 291–307, 1969.
[16] X. Huang, D. Chen, T. Ren, dan D. Wang, A survey of community detection methods in multilayer networks, vol. 35, no. 1. Springer US, 2021.
[17] S. Fortunato, “Community detection in graphs,” Phys. Rep., vol. 486, no. 3–5, hal. 75–174, 2010, doi: 10.1016/j.physrep.2009.11.002.