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Complex network community mining method based on local minimum edges

A local minimum and complex network technology, applied in the field of computer networks, can solve problems such as high time complexity

Inactive Publication Date: 2014-10-15
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method designs community search methods for bipartite networks and unipartite networks. Although it can more accurately divide the community structure in complex networks, it is necessary to test multiple possible sub-community combinations every time a community is merged. , the time complexity is high

Method used

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  • Complex network community mining method based on local minimum edges
  • Complex network community mining method based on local minimum edges
  • Complex network community mining method based on local minimum edges

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Embodiment Construction

[0050] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation methods and processes are given, but the protection scope of the present invention is not limited to the following embodiments.

[0051] In this embodiment, community structure mining is carried out on the classic social network data set—Zachary Karate Club Network. Include the following steps:

[0052] 1. Calculate the correlation coefficients between all pairs of nodes that are directly connected in the network, and use these correlation coefficients as the weight of each connection edge to calculate the value of the modularity under the current network community division.

[0053] 1.1. Take the adjacency matrix A of the complex network as input data. Zachary Karate Club network G contains 34 nodes and 78 edges, so the adjacen...

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Abstract

The invention provides a complex network community mining method based on local minimum edges. The complex network community mining method comprises the following steps: obtaining an adjacent matrix A of a complex network; calculating a similarity matrix R; carrying out community detection C to a complex network diagram G; looking up one group of local minimum edges; detecting local topological structures on two end points of each local minimum edge, and confirming and removing the local minimum edges which enable a current community structure to more conform to a community definition; detecting whether a new connected subgraph appears in the network, recalculating a weight of each edge if new connected subgraph does not appear in the network, and judging whether division is reasonable if new connected subgraph appears in the network; and if the division is unreasonable, outputting a result, recalculating the weight of each edge if the division is reasonable, and carrying out a next iteration process. The invention has the characteristics of being high in precision, high in speed and good in universality.

Description

technical field [0001] The invention relates to the technical field of computer networks, in particular to a complex network community mining method based on local minimum edges. Background technique [0002] A complex network is an abstract representation of complex systems in the real world. It is composed of nodes and links between nodes. Nodes represent individuals in the complex system, and links between nodes represent the relationship between nodes. At present, complex networks have been widely used in structural modeling of complex systems such as the Internet, social networks, and biological networks. [0003] An important topological property of complex networks is community structure. The community structure is a set representing several nodes in a complex network. The nodes in this set are closely connected, while the edges between different community structures (ie, different sets) are relatively sparse. The community structure of a complex network can reflect...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/958G06Q50/01
Inventor 李生红张爱新李琳张月国
Owner SHANGHAI JIAO TONG UNIV
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