The node importance evaluation method based on graph convolution in multilayer heterogeneous networks

ABSTRACT

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).

KEYWORDS:

• Multi-layer network
• critical node evaluation
• graph convolution
• meta path

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive and insightful comments on the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 62062050], the Natural Science Foundation of Jiangxi Province [grant number 20202B ABL2020 39] and the Innovation Foundation for Postgraduate Student of Jiangxi Province [grant number YC2021-S709].