The node importance evaluation method based on graph convolution in multilayer heterogeneous networks
Node importance evaluation is a hot issue in complex network analysis. Existing node importance evaluation methods are mainly oriented to homogeneous networks, which ignore the heterogeneity of node types and edges. We propose an MLN critical node evaluation method based on graph convolution. In thi...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2229964 |
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| author | Zhixing Chen Jian Shu Linlan Liu |
| author_facet | Zhixing Chen Jian Shu Linlan Liu |
| author_sort | Zhixing Chen |
| collection | DOAJ |
| description | Node importance evaluation is a hot issue in complex network analysis. Existing node importance evaluation methods are mainly oriented to homogeneous networks, which ignore the heterogeneity of node types and edges. We propose an MLN critical node evaluation method based on graph convolution. In this paper, we generate the feature matrix of nodes. Considering the diversity of node types in the network, we design an adapted node sampling method based on the meta path. An MLN node embedding model is constructed based on a graph convolutional network (MGC). Besides, the negative sampling technique is used to complete MGC training. Metrics of critical node evaluation are constructed by combining the node embedding vectors and local structural features to evaluate the node's importance. The experimental results show that the proposed method has better evaluation accuracy than the K-Shell algorithm (K-Shell), K-shell-based gravity model ranking algorithm (KSDG), the Page Rank algorithm in MLN (PR), influence maximization based on network embedding (IMNE) and the node ranking algorithm based on information entropy (ERM). |
| format | Article |
| id | doaj-art-4c844532c5f042eca7996c4e5d93417d |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-4c844532c5f042eca7996c4e5d93417d2025-08-20T01:56:46ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.22299642229964The node importance evaluation method based on graph convolution in multilayer heterogeneous networksZhixing Chen0Jian Shu1Linlan Liu2Nanchang Hangkong UniversityNanchang Hangkong UniversityNanchang Hangkong UniversityNode importance evaluation is a hot issue in complex network analysis. Existing node importance evaluation methods are mainly oriented to homogeneous networks, which ignore the heterogeneity of node types and edges. We propose an MLN critical node evaluation method based on graph convolution. In this paper, we generate the feature matrix of nodes. Considering the diversity of node types in the network, we design an adapted node sampling method based on the meta path. An MLN node embedding model is constructed based on a graph convolutional network (MGC). Besides, the negative sampling technique is used to complete MGC training. Metrics of critical node evaluation are constructed by combining the node embedding vectors and local structural features to evaluate the node's importance. The experimental results show that the proposed method has better evaluation accuracy than the K-Shell algorithm (K-Shell), K-shell-based gravity model ranking algorithm (KSDG), the Page Rank algorithm in MLN (PR), influence maximization based on network embedding (IMNE) and the node ranking algorithm based on information entropy (ERM).http://dx.doi.org/10.1080/09540091.2023.2229964multi-layer networkcritical node evaluationgraph convolutionmeta path |
| spellingShingle | Zhixing Chen Jian Shu Linlan Liu The node importance evaluation method based on graph convolution in multilayer heterogeneous networks Connection Science multi-layer network critical node evaluation graph convolution meta path |
| title | The node importance evaluation method based on graph convolution in multilayer heterogeneous networks |
| title_full | The node importance evaluation method based on graph convolution in multilayer heterogeneous networks |
| title_fullStr | The node importance evaluation method based on graph convolution in multilayer heterogeneous networks |
| title_full_unstemmed | The node importance evaluation method based on graph convolution in multilayer heterogeneous networks |
| title_short | The node importance evaluation method based on graph convolution in multilayer heterogeneous networks |
| title_sort | node importance evaluation method based on graph convolution in multilayer heterogeneous networks |
| topic | multi-layer network critical node evaluation graph convolution meta path |
| url | http://dx.doi.org/10.1080/09540091.2023.2229964 |
| work_keys_str_mv | AT zhixingchen thenodeimportanceevaluationmethodbasedongraphconvolutioninmultilayerheterogeneousnetworks AT jianshu thenodeimportanceevaluationmethodbasedongraphconvolutioninmultilayerheterogeneousnetworks AT linlanliu thenodeimportanceevaluationmethodbasedongraphconvolutioninmultilayerheterogeneousnetworks AT zhixingchen nodeimportanceevaluationmethodbasedongraphconvolutioninmultilayerheterogeneousnetworks AT jianshu nodeimportanceevaluationmethodbasedongraphconvolutioninmultilayerheterogeneousnetworks AT linlanliu nodeimportanceevaluationmethodbasedongraphconvolutioninmultilayerheterogeneousnetworks |