Explore Network Community Using a High-dimensional Degree-corrected Stochastic Blockmodel
碩士 === 國立交通大學 === 應用數學系所 === 99 === If the pairwise relationship can be measured, then all data points can be linked or unlinked to each other determined by that pairwise relation. The network structure can be constructed accordingly. The investigation of this kind of network data becomes crucial in...
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| Format: | Others |
| Language: | en_US |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/71839891670360547095 |
| Summary: | 碩士 === 國立交通大學 === 應用數學系所 === 99 === If the pairwise relationship can be measured, then all data points can be linked or unlinked to each other determined by that pairwise relation. The network structure can be constructed accordingly. The investigation of this kind of network data becomes crucial in understanding the interrelationship that is hidden in the data. Protein–protein interaction networks and large scale social networks are typical examples of this type of data where interrelationships in the data can be hidden and indirect. Consequently, we need flexible models to explain the structure of complex networks. In this study, we introduce a probability model that integrates the important properties of the standard stochastic blockmodel and degree-corrected stochastic blockmodel in literature to form the high dimensional degree corrected stochastic blockmodel. Thus, this model can represent the global structure of networks and reflect the local properties of vertices. Empirical studies are conducted to evaluate the performance of the proposed model for real data.
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