article · Information Sciences
Influence maximisation algorithms identify the most critical nodes for spreading information across networks, but existing solutions face trade-offs between computational speed and analytical precision. Greedy approaches are constrained by poor scalability, whereas heuristic methods compromise on accuracy. To address this challenge, a Gaussian propagation model designed for social networks simulates information dissemination across a multi-dimensional space shaped by offset, motif, and degree dimensions. The propagation behaviour in this space is governed by specific diffusion parameters. An influence maximisation algorithm built around this model applies an improved CELF algorithm to accelerate computation. Supported by theoretical proofs and tested through extensive comparative experiments against established methods, the resulting algorithm achieves substantial gains in both operational efficiency and accuracy.
Understanding how information spreads across complex networks is vital for identifying influential participants and directing communication effectively. By successfully balancing computational speed with high accuracy, improved influence maximisation algorithms allow analysts to model large, interconnected communities much more reliably without encountering prohibitive processing bottlenecks or generating inaccurate predictions.
This algorithmic method could support developers of network analytics platforms, social media intelligence tools, and marketing software that require rapid identification of key network nodes. The work currently represents early-stage algorithmic research, evaluated through theoretical proofs and controlled computer experiments, meaning further engineering would be required before it could be integrated into commercial software environments.
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The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency.
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DOI: 10.1016/j.ins.2021.04.061
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