衡量“热点”的影响
Measuring the impact of hits
AI总结：
本文提出一种稳健统计方法量化增长网络中节点竞争链接的强度，应用于在线新闻评论和电影票房数据，发现从完全弹性到中间影响的行为谱系，且完全竞争从未出现，对复杂系统建模和电子商务有直接意义。
AI中文摘要：
许多真实系统可以被表示为增长网络，其中新节点和链接逐渐涌现。Barabási-Albert增长网络模型以及许多受其启发的模型，都基于节点竞争链接的观点。然而，这种竞争的强度及其存在本身尚未被检验。我们提出了一种稳健的统计方法来量化节点竞争链接的强度，并将其应用于各种真实系统的数据——包括在线新闻评论和电影票房数据。我们发现了从完全弹性情况（新进入者以不影响系统其余部分的方式塑造网络增长）到中间情况（新进入者可测量地影响其余部分）的一系列可能行为。完全竞争从未被观察到。这些发现对复杂系统建模和电子商务应用具有直接意义。
英文摘要：
Many real systems can be represented as growing networks where new nodes and links gradually emerge. The Barabási-Albert model for growing networks, and many models inspired by it, are based on the idea that nodes compete for links. However, the strength and the very presence of this competition have not been tested. We propose a robust statistical approach to quantify how strongly nodes compete for links, and apply it to data from various real systems---commenting on online news and cinema attendance data. We find a range of possible behaviors, from the perfectly elastic case, where new entrants shape network growth in a way that leaves the rest of the system unaffected, to an intermediate case where new entrants measurably affect the rest. Perfect competition is never observed. These findings have direct implications for complex systems modeling and e-commerce applications.
