Unsupervised community discovery method based on central node graph convolutional network

A technology of central node and community discovery, applied in other database retrieval, other database index, other database query, etc., can solve the problem of not considering the importance of community central nodes and central node clusters, so as to improve the division ability and improve the modularity Effect

Pending Publication Date: 2022-07-29
TAIYUAN UNIV OF TECH
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  • Application Information

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Problems solved by technology

Analysis of existing methods, although the introduction of GCN in community detection has achieved good results, but the importance of community center nodes and center node clusters in community monitoring problems has not been considered

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  • Unsupervised community discovery method based on central node graph convolutional network
  • Unsupervised community discovery method based on central node graph convolutional network
  • Unsupervised community discovery method based on central node graph convolutional network

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Embodiment Construction

[0030] In order to have a clearer understanding of the technical features, objects and effects of the present invention, the specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0031] like figure 1 and figure 2 As shown, the present invention designs a central node-based GCN unsupervised community discovery method. First, the "center-expansion" algorithm is used to expand the nodes with more common neighbors and similar community memberships on the basis of determining the central node. , forming a central node cluster. Then, use these central node clusters to train the GCN model, and use the trained GCN model to perform clustering or community discovery on the entire network nodes. Specific steps include:

[0032] Step 1. Construct a network G=(V, E), where V and E represent sets of nodes and edges, respectively; vertex attribute X, let X∈R |v|×q to contain features as x v A matrix of nodes, each r...

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Abstract

The invention discloses an unsupervised community discovery method based on a central node graph convolutional network, and the method comprises the steps: constructing a detection model CN-GCN, employing a center-extension algorithm to extend nodes which have more common neighbors and have similar community member identities on the basis of determining a central node, and forming a central node cluster; and training a GCN model by using the center node cluster, and carrying out clustering or community discovery on the whole network nodes by using the trained GCN model. The community center node cluster can accommodate nodes with more common neighbors and similar community member identities, and the nodes have similar attributes, so that the modularity of the community sub-graph is improved; according to the invention, the CN-GCN model of the community center node is combined, so that the division capability of the node field can be improved.

Description

technical field [0001] The invention relates to the field of graph convolution network GCN unsupervised community discovery, in particular to a central node-based GCN unsupervised community discovery method. Background technique [0002] Complex networks such as biological networks, communication networks, and social networks are abstract representations of biological systems, communication systems, and interaction systems, respectively. Networks are both a representation and an analytical tool for in-depth understanding of complex systems. One of the most important characteristics of complex networks is their community structure. In recent years, network community detection has become a research hotspot in the field of complex networks. A network community is defined as a group of closely connected nodes that play a very important role in the network. The goal of community detection is to assign each node in the network to a community based on network topology, node simila...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/903G06F16/901G06F16/906
CPCG06F16/90335G06F16/906G06F16/9024
Inventor 邓丽平郑文崔佳梅刘彦君
Owner TAIYUAN UNIV OF TECH
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