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Dynamic community discovery method for phylogenetic transplantation partition sequential network

A time series network, dynamic community technology, applied in the field of network science, to achieve the effect of improving accuracy

Active Publication Date: 2021-06-04
宽泛科技(盐城)有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Currently, no dynamic community discovery algorithm can fully identify and track all these evolutionary events

Method used

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  • Dynamic community discovery method for phylogenetic transplantation partition sequential network
  • Dynamic community discovery method for phylogenetic transplantation partition sequential network
  • Dynamic community discovery method for phylogenetic transplantation partition sequential network

Examples

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Embodiment Construction

[0086] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0087] According to the research of Aynaud et al., the existing dynamic community discovery algorithms can be divided into three categories: two-stage algorithms, evolutionary clustering and coupling networks. While Hartmann et al. argue that all existing dynamic community discovery methods can be identified as either online or offline methods. Rossetti and Cazabet present a recent survey on community detection in dynamic networks, which presents the unique capabilities and challenges that dynamic community detection algorithms have.

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Abstract

The invention provides a dynamic community discovery method for a phylogenetic transplantation partition sequential network. The method comprises the following steps: S0, defining a social network to obtain a sequential social network of the social network; the method comprises the following steps: S1, collecting data information of a current community, and taking the data information as to-be-processed community information; s2, preprocessing the to-be-processed community information obtained in the step S1; s3, constructing an error function, and then performing minimization processing on a quadratic form of the error function; judging the reliability range; s4, solving the gradient of the error function, and carrying out iteration according to the gradient direction; and S5, obtaining data information of the subregion communities. Compared with a FaceNet method, an SBM + MLE method, a CLBM method and a PisCES method, the PPPM model provided by the invention has the advantages that the accuracy is improved by 5% and 3% on an artificial network and a real network respectively, so that the provided PPPM model has robustness, is reasonable and effective, and can also be applied to the field of common social network community discovery.

Description

technical field [0001] The invention relates to the field of network science and technology, in particular to a dynamic community discovery method for system evolution and transplantation of partitioned sequential networks. Background technique [0002] Complex network analysis is gaining increasing attention among researchers in diverse fields such as computer science, social science, biological science, and physical science. Complex networks are always composed of nodes and edges, representing objects and interactions between objects, respectively. For example, in a social network, a node may be a social user, and an edge represents a following or being followed relationship between users. As one of the most important and powerful data structures, analyzing and modeling complex networks can be used in many tasks, such as social interaction pattern analysis, social recommendation and protein functional module identification. So far, the most fundamental tasks in complex n...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06F17/16G06F17/18G06Q50/00
CPCG06F17/16G06F17/18G06Q50/01G06F16/9536
Inventor 刘小洋张梦瑶丁楠
Owner 宽泛科技(盐城)有限公司
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