| Summary: | 碩士 === 國立交通大學 === 統計學研究所 === 105 === As graph data gain great popularity in the last decade, network analysis becomes an important research work. What’s more, in the big data era, han- dling large graph is the next challenge. Many researches have been working on exploring graph data starting by community detection. Poisson Model provides a di erent view of point to carry out the detection by assuming the raw networks are multi-edged. Although some weight information is missing and only single edges are observed, we design a mechanism to estimate the weight. Then we assume each node has a feature of propensity to connect to other nodes and take advantage of the fast optimization technics of Ball et al. for parameter estimation. In a sense, our model can be regarded as a generalized Ball et al.’s model. Conditional EM algorithm is applied to carry out the estimation. Next, AICc is served as our model selection criteria for choosing number of groups. The computational complexity is O(N2K). Ac- cording to the results of synthesized and real data, our method is e ective and fast. Compared to other optimization algorithm, the required number of iteration of EM algorithm is relatively fewer, therefore having a potential to be applied to large graphs.
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