Cell discovering method suitable for noise network

Abstract

The present invention belongs to the field of social network technologies and discloses a cell discovering method suitable for a noise network, comprising: calculating importance values of nodes in a network; establishing a set of core points and a set of boundary points; constructing priori information by selecting core representative points; selecting the boundary representative points to construct the prior information; combining the priori information into an extreme value optimization process; dividing the network into two parts with roughly equal nodes according to the topological structure; forming an initial cell structure; calculating the contribution of each node to the cell module density; moving the nodes with the smallest contribution to the other part for self-organizing optimization; repeating this self-organizing optimization process until the network's module density value no longer increases; and removing the connection edge between two finally obtained cells until the module density value of the entire network reaches the maximum. The present invention effectively improves the accuracy of cell division at a relatively small cost and improves the robustness of cell division in a noisy environment.

Description

technical field [0001] The invention belongs to the technical field of social networks, and in particular relates to a community discovery method suitable for noise networks. Background technique [0002] Many networks in the real world, such as telephone networks, email networks, and criminal networks, often contain some wrong or missing individual connections due to the difficulty of obtaining accurate and complete network structure information. Such networks are called noise networks. Most of the current community discovery methods discover the community structure in the network according to the connection relationship between nodes in the network. Since these methods completely depend on the topology of the network, they cannot be applied to noisy networks. When the proportion of noise in the network increases, the ability to discover the real community structure will decline rapidly; in the real network environment, part of the prior knowledge of community division is ...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the cost is high, and the obtained prior information may not have a strong guiding effect; the current community discovery methods cannot be applied to noisy networks and discover the real community structure. The ability of the network will decline rapidly with the increase of the noise ratio in the network; when obtaining prior information, high-quality prior information cannot be obtained with a small labor cost, which reduces the accuracy of community discovery

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