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- W3024282324 abstract "Many large networks, such as social networks and web graphs, have been found to exhibit a variety of different interesting features. The dynamic properties of large networks, such as the propagation and robustness feature, have been actively investigated over the past years. In our research, we attempted to understand the propagation and robustness feature of large networks quantitatively. Specifically, we employed a critical parameter based on network topology, Edge Instability (EI), to study the propagation feature. Our experimental results indicate that, compared with the existing propagation strategies, the EI-based strategy has a more serious impact on information propagation in large networks. Furthermore, we investigated the robustness performance of large networks by evaluating their connectivity in the scenario that some edges are removed gradually. The experimental results show that, among the existing edge removing strategies, removing edges with high EI hurts network robustness most. Finally, in order to understand the influence of the average EI of a network, we devised an evolution model to generate large networks with varied average EI. We found that the average EI has a significant impact on information propagation. However, it has an unnoticeable influence on network robustness when EI-based strategies are adopted." @default.
- W3024282324 created "2020-05-21" @default.
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- W3024282324 date "2020-10-01" @default.
- W3024282324 modified "2023-10-02" @default.
- W3024282324 title "Edge instability: A critical parameter for the propagation and robustness analysis of large networks" @default.
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- W3024282324 doi "https://doi.org/10.1016/j.ins.2020.05.027" @default.
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