Abstract
In this chapter, we explore various types of structural regularities in networks. For this aim, we divide network nodes into groups such that the members of each group have similar patterns of connections to other groups. Then, we leverage generative model to describe network structure. The structural regularities are then naturally obtained by statistical inference using expectation maximization algorithm. The most prominent strength of our model is the high flexibility, which enables it to possess the advantages of existing models and to overcome their shortcomings in a unified way. Not only can broad types of structure be detected without prior knowledge of the type of intrinsic regularities existing in the target network, but also the type of identified structure can be directly learned from the network. Our model outperforms the state-of-the-art model in shedding light on various structural regularities of networks, which are beyond the capability of existing models.
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Notes
- 1.
Since this network is an undirected network, the two kinds of belonging coefficient are identical, i.e., α ir =β ir .
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Shen, HW. (2013). Exploratory Analysis of the Structural Regularities in Networks. In: Community Structure of Complex Networks. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31821-4_5
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