並列摘要
Due to the rapid progress of information and communication technologies, networking between individuals and machines has become necessary and crucial. Some phenomenon already exists in the real world, such as epidemics, social networks, ecology food web, metabolic, genetic or protein interaction networks and database maintained by telecom operator can also be observed via scientific approaches based on networking aspect, which may be useful for solving problems or providing killer applications in real world. In this paper we first review some basic concepts of characterizing a network from different aspects by various structural properties. Then we describe several important network models and approaches developed in the past decade. Furthermore, in order to better obtain network structural properties and topology from data in an effective way, we visualized the result of network analysis by open-source software library, JUNG as a tool, and take cognitive radio networks as a target for evaluation of the structural properties. Finally we apply Bayesian method to the data from real mobile service network to estimate the preference of subscribers for achieving precise marketing as an example of probabilistic network construction from data.
延伸閱讀
- Wang, M. G., Wang, X. Y., & Liu, Z. Z. (2010). A New Complex Network Model with Hierarchical and Modular Structures. Chinese Journal of Physics, 48(6), 805-813. https://www.airitilibrary.com/Article/Detail?DocID=05779073-201012-201102170016-201102170016-805-813
- Chang, C. J. (2014). Algorithms for Constructing and Characterizing Causal Networks [doctoral dissertation, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2014.02663
- Yeh, I. C., Lin, K. C., Huang, K. C., Zhang, X. Y., & Wu, C. (2011). Elliptical Probabilistic Neural Networks. Journal of Information Technology and Applications(資訊科技與應用期刊), 5(2), 92-102. https://doi.org/10.6302/JITA.201106_5(2).0005
- Chen, Y. W., & Chou, C. C. (2003). Correlation Based Traffic Modeling of Sub-Networks. 網際網路技術學刊, 4(4), 277-283. https://doi.org/10.6138/JIT.2003.4.4.09
- Lu, P. E. (2020). A Unified Probabilistic Framework for Network Analysis and Learning [doctoral dissertation, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-0209202114070265