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MLGM: A Novel Gravity Model for Node Importance Evaluation based on the Integrated Characteristics of Nodes

Hui An
Frontiers in Science and Engineering, (2026), Vol.6, No.1, pp.32-50
Published: January 24, 2026
DOI: 10.54691/zpetzz56
PDF: Download Full Text PDF
Abstract

Identifying key nodes in complex networks is a fundamental and challenging problem in network science, and it has garnered widespread interest over the past few years. The existing centrality methods in assessing node importance still are inadequate in comprehensively utilizing node features, and struggle to achieve optimal performance across different network types. In response, this paper proposes a gravity centrality based on node comprehensive features and node weight. The model incorporates the local information and positional attributes of a node into its quality, and considers using the maximum eigenvalue as the node's weight to reflect its global influence in the network, which effectively addresses the inherent heterogeneity of nodes. To validate the accuracy and effectiveness of this model in identifying key nodes, the paper compares the proposed gravity centrality with traditional centrality methods across six real-world network datasets from multiple evaluation perspectives. The experimental results confirm the strong precision and effectiveness of the proposed method in recognizing pivotal nodes.

Keywords: Identification of Key Nodes, Gravity Centrality, Multiple Attributes, Node‘s Weight.
APA Citation: Hui An (2026). MLGM: A Novel Gravity Model for Node Importance Evaluation based on the Integrated Characteristics of Nodes. Frontiers in Science and Engineering, 6(1), 32-50. https://doi.org/10.54691/zpetzz56

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