We study the impact of seeds on cooperate epidemic spreading on complex networks. A cooperative spreading model is proposed, in which two diseases are spreading simultaneously. Once the nodes are infected by one disease, they will have a larger probability of being infected by the other. Besides, we adopt five different selection strategies to choose the seeds, and the set size of seeds is fixed at five nodes. Through extensive Monte Carlo simulations, we find that the final fraction of nodes that have been infected by one or both diseases display continuous phase transition on both synthetic networks and real-world networks, and the selection strategy does not alter the transition type. Besides, we find that the eigenvector centrality promotes the cooperative spreading on the artificial network, and the degree centrality promotes the spreading of the two cooperative diseases on the real-world networks. The results of this study are of great significance for the development of the targeted strategies of disease control.

中文翻译：

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种子对复杂网络协同流行病传播的影响

我们研究了种子对复杂网络上合作流行病传播的影响。提出了一种合作传播模型，其中两种疾病同时传播。一旦节点被一种疾病感染，它们将有更大的概率被另一种疾病感染。此外，我们采用五种不同的选择策略来选择种子，种子的集合大小固定在五个节点。通过广泛的蒙特卡罗模拟，我们发现被一种或两种疾病感染的最后一部分节点在合成网络和现实世界网络上都显示出连续的相变，并且选择策略不会改变转换类型。此外，我们发现特征向量中心性促进了人工网络上的合作传播，度中心性促进了两种协同疾病在现实世界网络中的传播。本研究结果对制定疾病控制的针对性策略具有重要意义。