Analisis Informasi Jaringan Homogen dan Heterogen pada Liga Champions UEFA

https://doi.org/10.35912/jisted.v1i2.1928
Published: May 2, 2023

Abstract:

Purpose: The interpretation of network analysis research can be challenging today. The aim of this study is to analyze the homogeneous and the heterogeneous network information that occurred in the UEFA Champions League 2017-2018.

Research Methodology: To obtain an interpretation of the results of network information analysis, centrality measurements and community detection were performed, where the centrality measurements methods used are Degree centrality, Betweenness centrality, Eigencentrality, PageRank, while community detection method used is performed using the Louvain.

Result: The homogenous and heterogeneous network analysis was conducted using dataset of 17980 players, 32 teams, and 128 matches in Champions League 2017-2018. In this analysis, homogenous and heterogeneous network schemes were used to represent objects and relationships between objects in the network. The analysis was based on centrality measurements to identify influential nodes and community emergence within the network. The result is an interpretation of network analysis in the form of information about the roles of players, teams, countries, locations, formations, and skills that affect the performance of UEFA Champions League.

Limitation: the use of diverse data sources, the application or development of data analysis techniques, and the formation of a broader network scheme

Contribution: Obtaining information related to the UEFA Champions League based on the interpretation result of the analysis of homogeneous and heterogeneous networks

Keywords:
1. Heterogeneous Network
2. Homogeneous Network
3. Network Analysis
4. UEFA Champion League
Authors:
1 . Rahman Taufik
2 . Muhaqiqin Muhaqiqin
3 . Igit Sabda Ilman
4 . Ridho Sholehurrohman
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How to Cite
Taufik, R., Muhaqiqin, M., Ilman, I. S., & Sholehurrohman , R. . (2023). Analisis Informasi Jaringan Homogen dan Heterogen pada Liga Champions UEFA . Jurnal Ilmu Siber Dan Teknologi Digital, 1(2), 91–110. https://doi.org/10.35912/jisted.v1i2.1928
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Vol. 1 No. 2 (2023): Mei
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References

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  2. Boden, B., Ester, M., & Seidl, T. (2014). Density-based subspace clustering in heterogeneous networks. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I 14 (pp. 149-164). Springer Berlin Heidelberg.
  3. Chunaev, P. (2020). Community detection in node-attributed social networks: a survey. Computer Science Review, 37, 100286.
  4. Hu, B., Fang, Y., & Shi, C. (2019). Adversarial learning on Heterogeneous Information Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 120-129).
  5. Huang, H., Shi, R., Zhou, W., Wang, X., Jin, H., & Fu, X. (2021). Temporal Heterogeneous Information Network Embedding. In IJCAI (pp. 1470-1476).
  6. Javed, M. A., Younis, M. S., Latif, S., Qadir, J., & Baig, A. (2018). Community detection in networks: A multidisciplinary review. Journal of Network and Computer Applications, 108, 87-111.
  7. Jin, D., Yu, Z., Jiao, P., Pan, S., He, D., Wu, J., & Zhang, W. (2021). A survey of community detection approaches: From statistical modeling to deep learning. IEEE Transactions on Knowledge and Data Engineering.
  8. Kong, X., Shi, Y., Yu, S., Liu, J., & Xia, F. (2019). Academic social networks: Modeling, analysis, mining and applications. Journal of Network and Computer Applications, 132, 86-103.
  9. Luo, C., Pang, W., & Wang, Z. (2014). Semi-supervised clustering on Heterogeneous Information Networks. In Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II 18 (pp. 548-559). Springer International Publishing.
  10. Qiu, C., Chen, W., Wang, T., & Lei, K. (2015). Overlapping community detection in directed heterogeneous social network. In Web-Age Information Management: 16th International Conference, WAIM 2015, Qingdao, China, June 8-10, 2015. Proceedings 16 (pp. 490-493). Springer International Publishing.
  11. Saxena, A., & Iyengar, S. (2020). Centrality measures in complex networks: A survey. arXiv preprint arXiv:2011.07190.
  12. Serrano, D. H., & Gómez, D. S. (2020). Centrality measures in simplicial complexes: Applications of topological data analysis to network science. Applied Mathematics and Computation, 382, 125331.
  13. Shi, C., Hu, B., Zhao, W. X., & Philip, S. Y. (2018). Heterogeneous Information Network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 31(2), 357-370.
  14. Shi, C., Li, Y., Zhang, J., Sun, Y., & Philip, S. Y. (2016). A survey of Heterogeneous Information Network analysis. IEEE Transactions on Knowledge and Data Engineering, 29(1), 17-37.
  15. Shi, D., Lü, L., & Chen, G. (2019). Totally homogeneous networks. National science review, 6(5), 962-969.
  16. Singh, R. R. (2022). Centrality measures: a tool to identify key actors in social networks. Principles of Social Networking: The New Horizon and Emerging Challenges, 1-27.
  17. Tsiotas, D., & Polyzos, S. (2015). Introducing a new centrality measure from the transportation network analysis in Greece. Annals of Operations Research, 227, 93-117.
  18. Zhang, J., Fei, J., Song, X., & Feng, J. (2021). An improved Louvain algorithm for community detection. Mathematical Problems in Engineering, 2021, 1-14.