Network community detecting method based on multi-objective memetic computation

A network community and detection method technology, applied in the network field, can solve problems such as local optimum and low detection accuracy, achieve the effects of accelerating convergence speed, improving search efficiency, and overcoming low initial accuracy

Active Publication Date: 2013-12-18
XIDIAN UNIV
View PDF6 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the detection accuracy is not high, and it is easy to fall into problems such as local optimum.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Network community detecting method based on multi-objective memetic computation
  • Network community detecting method based on multi-objective memetic computation
  • Network community detecting method based on multi-objective memetic computation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] The present invention will be further described below in conjunction with the accompanying drawings.

[0041] Refer to attached figure 1 , the steps of the present invention are as follows.

[0042] Step 1, establish the adjacency matrix of the network to be detected.

[0043] The nodes in the network to be detected are numbered sequentially from 1 to N, and N represents the total number of nodes in the network; the element a in the adjacency matrix corresponding to the edge connected between node i and node j in the network is ij Set to 1; element a in the adjacency matrix corresponding to the unconnected edge between node i and node j in the network ij Set to 0; get the adjacency matrix corresponding to this network.

[0044] Step 2, network population initialization.

[0045] Using the label method, the adjacency matrix is ​​classified into communities, and the network community division results with different label values ​​assigned to each node are obtained. Th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a network community detecting method based on multi-objective memetic computation, which mainly solves the problems that the traditional method is not high in resolution ratio, so local optimum is easily caused, further only a single division result is obtained, a hierarchical structure of a network cannot be obtained, and the like. The method has the realization steps: (1) establishing an adjacency matrix of a to-be-detected network; (2) initiating a network population; (3) generating a new individual; (4) updating the network population; (5) locally searching the network population; (6) judging whether cyclic algebra is reached or not; (7) calculating the modularity value of each individual in the network population; (8) detecting communities obtained after network division is carried out. The network community detecting method has the beneficial effects that the network population is initiated by adopting a labeling method and combining a multi-objective evolutionary algorithm and a stimulated annealing algorithm based on discomposition, the initial detection precision of the network is improved, the convergence of the algorithm is accelerated, the local optimization capability of the algorithm is improved, the local optimum is avoided, the resolution ratio of the algorithm is improved, and the hierarchical structure of the network can be found.

Description

technical field [0001] The invention belongs to the field of network technology, and further relates to a network community detection method based on multi-objective cipher calculation in the field of data mining. The invention can be used for network community structure detection and analysis. Background technique [0002] Networks are an effective form of representing the interrelationships among objects in many systems in the real world. Such as collaborative network, World Wide Web, biological network, communication network, transportation network, social network, etc., these systems can be represented by complex networks. Studying the community structure of complex networks not only has very important theoretical significance for analyzing network topology, understanding network functions, discovering hidden laws in networks, and predicting network behavior, but also has broad application prospects. It has been applied to metabolic networks Analysis, protein interactio...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N3/00G06Q10/10
Inventor 马文萍焦李成云杰郝金现马晶晶公茂果
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap