Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, 410073, China.
Unit 66136, Beijing, 100042, China.
Sci Rep. 2019 May 13;9(1):7305. doi: 10.1038/s41598-019-43853-9.
The identification of the most influential nodes has been a vibrant subject of research across the whole of network science. Here we map this problem to structured evolutionary populations, where strategies and the interaction network are both subject to change over time based on social inheritance. We study cooperative communities, which cheaters can invade because they avoid the cost of contributions that are associated with cooperation. The question that we seek to answer is at which nodes cheaters invade most successfully. We propose the weighted degree decomposition to identify and rank the most influential invaders. More specifically, we distinguish two kinds of ranking based on the weighted degree decomposition. We show that a ranking strategy based on negative-weighted degree allows to successfully identify the most influential invaders in the case of weak selection, while a ranking strategy based on positive-weighted degree performs better when the selection is strong. Our research thus reveals how to identify the most influential invaders based on statistical measures in dynamically evolving cooperative communities.
在整个网络科学领域，识别最具影响力的节点一直是一个活跃的研究主题。在这里，我们将这个问题映射到结构化进化群体中，其中策略和交互网络都随着时间的推移基于社会传承而发生变化。我们研究了合作社区，因为合作者避免了合作相关的贡献成本，所以骗子可以入侵这些社区。我们要回答的问题是，在哪些节点上骗子入侵得最成功。我们提出了加权度分解来识别和排名最具影响力的入侵者。更具体地说，我们根据加权度分解区分了两种排名。我们表明，在弱选择的情况下，基于负加权度的排名策略可以成功地识别最具影响力的入侵者，而在选择较强的情况下，基于正加权度的排名策略表现更好。因此，我们的研究揭示了如何基于动态进化合作社区中的统计度量来识别最具影响力的入侵者。
