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Label propagation community finding algorithm based on node importance degrees

A technology of node importance and community discovery, applied in the field of label propagation community discovery algorithm based on node importance, to shorten the iteration cycle and improve the quality

Inactive Publication Date: 2017-09-22
TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art, and provide a label propagation community discovery algorithm based on node importance, which can significantly improve the quality of community discovery and shorten the iteration cycle, thereby improving the accuracy of community discovery sex and stability

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  • Label propagation community finding algorithm based on node importance degrees
  • Label propagation community finding algorithm based on node importance degrees
  • Label propagation community finding algorithm based on node importance degrees

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Embodiment Construction

[0032] Embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings:

[0033] A label propagation community discovery algorithm based on node importance, comprising the following steps:

[0034] Step 1: Initialize the unique label for each node v ∈ V, c i =i.

[0035] In this embodiment, the labels of the six nodes 1, 2, 3, 4, 5, and 6 are initialized as a, b, c, d, e, and f respectively, such as Figure 1a shown.

[0036] Step 2: Calculate the importance of each node according to the importance calculation method, and sort the nodes according to the importance of the nodes from high to low to generate an ordered sequence V'={v l ,v s ,···,v n}, where NI(v l )≥NI(v s )≥···≥NI(v n ).

[0037] In this step, calculating the importance of each node is a new importance calculation method based on the importance obtained by prior attributes, and its calculation formula is as follows:

[0038]

[0039] Among them...

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Abstract

The invention relates to a label propagation community discovery algorithm based on node importance, and its main technical features are: initializing the unique label of each node; calculating the importance of each node, and sorting the nodes according to the node importance from high to low, Generate an ordered sequence; set the number of iterations t=1; for any node in the ordered sequence, update the label of the node to the label with the greatest influence in the label set of adjacent nodes according to the label selection method and label update rule; if the number of iterations t==max Iter or the label of each node is the most influential label, then the nodes with the same label are classified into the same community, and the process ends; otherwise, the number of iterations t is increased by 1, and the update is continued. The present invention has reasonable design, can significantly improve the quality of community discovery under the condition of similar complexity, shorten the iteration cycle, has high accuracy and stability, and can be widely used in community discovery, social network and other fields.

Description

technical field [0001] The invention belongs to the technical field of intelligent information processing, in particular to a tag propagation community discovery algorithm based on node importance. Background technique [0002] Community discovery in social networks is of great significance for social network analysis. In the past 10 years, many social network community discovery methods have been proposed. According to different solving strategies, they can be mainly divided into optimization-based community discovery methods and heuristic-based community discovery methods. Optimization-based methods set the objective function and Iterative approximation to the optimal value of the function realizes community discovery, and representative methods include spectral method and modularity maximization method. The method based on heuristic strategy finds the optimal community division by setting heuristic rules, representative algorithms such as GN (Girvan-Newman) algorithm and...

Claims

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Application Information

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IPC IPC(8): G06Q50/00
CPCG06Q50/01
Inventor 张贤坤任静荚佳宋琛张倩
Owner TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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