摘要
FEC adopts an agent-based heuristic that makes the algorithm efficient and is presented with two phases that are Finding Community (FC) and Extraction Community (EC). Although designed with linear running time, original FEC can not obtain ideal results on the graph whose community structure is not well defined. This paper extend FEC as E_FEC to seek a good trade-off between effectiveness and efficiency. In FC phase, we calculate the accumulative transition probability to find the existence of communities, and propose an automatic selection algorithm for the sink node. In EC phase, we present another simpler cut criterion based on Average cut (Acut) which costs less running-time in EC phase. The performance of E_FEC is rigorously validated through comparisons with other representative methods against both synthetic and real-world networks with different scales.
被引用紀錄
延伸閱讀
- Zhao, Z., Guo, Y., & Zhao, W. (2015). Community Detection in Directed Weighted Function-Call Networks. International Journal of Automation and Control Engineering, 4(1), 9-13. https://doi.org/10.14355/ijace.2015.0401.03
- Tu, R., Zhu, Z., & Bai, Y. (2020). Improved Pedestrian Detection Algorithm Based on HOG and SVM. 電腦學刊, 31(4), 211-221. https://doi.org/10.3966/199115992020083104016
- 周家正（2005）。Study On A Variable Frame Pitch Detection Algorithm〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu200500710
- 吳牧寰 (2013). A Markov Chain Approach for Relative Centrality and Community Detection [master's thesis, National Tsing Hua University]. Airiti Library. https://doi.org/10.6843/NTHU.2013.00732
- Shen, B., Wang, N. W., & Qiu, H. H. (2014). A New Genetic Algorithm for Overlapping Community Detection. 網際網路技術學刊, 15(7), 1143-1150. https://doi.org/10.6138/JIT.2014.15.7.07