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A Dynamic Community Discovery Method for Phylogenetic Transplanted Partitioned Temporal Networks

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

Active Publication Date: 2021-09-28
宽泛科技(盐城)有限公司
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  • Summary
  • 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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  • A Dynamic Community Discovery Method for Phylogenetic Transplanted Partitioned Temporal Networks
  • A Dynamic Community Discovery Method for Phylogenetic Transplanted Partitioned Temporal Networks
  • A Dynamic Community Discovery Method for Phylogenetic Transplanted Partitioned Temporal Networks

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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 present invention proposes a dynamic community discovery method for systematic evolution and transplantation of partitioned time-series networks, including: S0, defining the social network to obtain its time-series social network; S1, obtaining the data information of the current community, and using it as community information to be processed; S2, preprocess the community information to be processed obtained by S1; S3, construct the error function, and then minimize the quadratic form of the error function; judge its trust range; S4, calculate the gradient of the error function, and according to the gradient The direction is iterated; S5, to obtain the partition community data information. Compared with FaceNet, SBM+MLE, CLBM, and PisCES methods, the PPPM model proposed by the present invention has improved the accuracy of 5% and 3% respectively on the artificial network and the real network, so the proposed PPPM model has robustness, and the PPPM model It is reasonable and effective, and the model can also be applied in the field of general 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 Patents(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