3 results match your criteria: "People's Liberation Army Strategic Support Force Information Engineering University[Affiliation]"
Article Synopsis
- Understanding information propagation in social networks is essential, but current methods often overlook the global dependencies and user interactions in propagation cascades.
- The proposed DropMessage Hypergraph Attention Networks model enhances prediction accuracy by constructing a hypergraph from the cascade sequence and utilizing user preference subgraphs that evolve over time.
- Experimental results demonstrate that this new model significantly outperforms existing methods in predicting user cascades and exhibits greater robustness against data changes.
Assessing the impact of isolation policies on epidemic dynamics through swarm entropy.
Front Public Health
February 2024
National Digital Switching System Engineering and Technological R&D Center, People's Liberation Army Strategic Support Force Information Engineering University, Zhengzhou, China.
Isolation policies are an effective measure in epidemiological models for the prediction and prevention of infectious diseases. In this paper, we use a multi-agent modeling approach to construct an infectious disease model that considers the influence of isolation policies. The model analyzes the impact of isolation policies on various stages of epidemic from two perspectives: the external environment and agents behavior.
View Article and Find Full Text PDFHypernetwork Link Prediction Method Based on Fusion of Topology and Attribute Features.
Entropy (Basel)
December 2022
National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450001, China.
Link prediction aims at predicting missing or potential links based on the known information of complex networks. Most existing methods focus on pairwise low-order relationships while ignoring the high-order interaction and the rich attribute information of entities in the actual network, leading to the low performance of the model in link prediction. To mine the cross-modality interactions between the high-order structure and attributes of the network, this paper proposes a hypernetwork link prediction method for fusion topology and attributes (TA-HLP).
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