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A dynamic propagation-based algorithm for node diffusion capacity evaluation in complex networks
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DOI：10.1007/s10586-026-06477-z.png)
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Abstract
En 中文
A novel dynamic propagation-based algorithm, Node Diffusion Capacity (NDC), is proposed to evaluate a node’s ability to propagate information in a network. Unlike traditional centrality measures, NDC captures dynamic propagation through three components: diffusion gain, diffusion loss, and diffusion stability, and quantifies diffusion capacity using the propagation distance distribution and the Wasserstein distance. Experiments on both weighted and unweighted networks show that NDC outperforms 13 benchmark algorithms in terms of final infection ratio, propagation duration, and average infection rate, with statistical significance confirmed by t-tests. Robustness tests on 17 real-world networks further validate NDC’s stability under node immunization and edge resistance conditions. Furthermore, synthetic network tests highlight NDC’s superiority in path survival rate, infection rate, redundancy, and propagation efficiency. This work provides a novel perspective on assessing node diffusion capacity in complex networks.
Keywords:
Diffusion capacity
Complex networks
Distance distribution
Wasserstein distance
Robustness analysis
Journal
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2.9
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221
Citations:
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