AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
Identifying influential nodes in complex networks is a fundamental problem with applications in information diffusion, epidemic control, infrastructure robustness, and biological systems. Traditional approaches rely on structural centrality measures, such as degree, betweenness, closeness, and PageRank, which quantify...
The identification of influential nodes in directed networks is fundamental to diffusion analysis, network robustness assessment, and information recommendation. Owing to the asymmetry introduced by directed edges, conventional centrality methods often struggle to jointly characterize the local spreading range, higher-...
Identifying influential nodes in complex networks is a fundamental problem in network science, with applications in epidemic control, information diffusion, and rumor spreading. K-shell decomposition is widely used due to its global perspective and linear time complexity. However, most K-shell based approaches are desi...
Experimental results in seven complex networks of the real world show that the proposed hybrid centrality called k-core neighborhood density (KND) remarkably balances rank distribution and accuracy, and the identified influential nodes have a superior ability to spread their influence over a wide area of a network.
The analysis of random walks on networks often relies on global quantities that average over nodes, thereby masking local differences in diffusion speed. This study introduces a vertex-level quantity Hi, defined as the finite-window fitted scaling exponent of the mean squared resistance distance ⟨Ωi2(t)⟩∼Cit2Hi from a...
Echo chamber affection would block information diffusion and cut down the success rate of delivery. It is of high value to model and identify the user nodes which are the edges of community in social networks as well as the susceptible ones in cascade propagation, especially for the scenario conducting viral marketing...
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