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Adaptive dissemination process in weighted hypergraphs
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AI
DOI：10.1016/j.eswa.2024.126340.png)
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Abstract
En 中文
Compared with general complex networks, the hypernetworks with hypergraphs as the underlying topological structures facilitate a more detailed description of the characteristics presented by increasingly complex real networks. This paper first introduces a more practical weighted hypergraph for the dissemination pattern of information in the network. An adaptive dissemination (AD) model that is more consistent with the actual dissemination process and has a wider applicability than existing models is innovatively proposed. Concurrently, this paper implements extensive simulation experiments under weighted hypergraphs generated by real data in diverse domains, thereby qualitatively and quantitatively comparing different parameters and models, and fully validates the rationality and effectiveness of the AD model. Further, the model is employed to address the practical problem concerning the identification of members or committees with substantial discourse or decision- making power in two houses of the United States Congress. Notably, the foregoing problem is reasonably modeled as an influence maximization problem, which is solved by a generalized greedy strategy and Monte Carlo simulation method under real data. The proposal of the AD model greatly supplements the scientific characterization of the dissemination process in weighted hypergraphs, which helps to better study various practical problems in hypergraphs and has great academic and application value.
Keywords:
Complex networks
Weighted hypergraphs
Adaptive dissemination
Influence maximization
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
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