论文标题
从部分信息中排名网络中有影响力的节点
Ranking influential nodes in networks from partial information
论文作者
论文摘要
许多复杂的系统表现出自然的层次结构，其中可以根据“影响力”的概念对元素进行排名。尽管通常需要对节点的影响的计算成分之间的相互作用的完整，准确的了解，但使用低级别近似值，我们表明，在各种情况下，有关节点邻域的本地信息足以可靠地估计它们的影响力，而无需推断或重建整个互动图。我们的框架在高精度上取得了成功，在像www pagerank，生态系统的营养水平，复杂经济体中工业部门的上游以及社交网络的集中度度量一样，只要基础网络的底层网络不存在极其稀疏，各种生态系统的营养水平，工业领域的上游以及社交网络的集中度度量。我们还讨论了这种“新兴地区”对非线性网络可观察物的近似计算的含义。
Many complex systems exhibit a natural hierarchy in which elements can be ranked according to a notion of "influence". While the complete and accurate knowledge of the interactions between constituents is ordinarily required for the computation of nodes' influence, using a low-rank approximation we show that in a variety of contexts local information about the neighborhoods of nodes is enough to reliably estimate how influential they are, without the need to infer or reconstruct the whole map of interactions. Our framework is successful in approximating with high accuracy different incarnations of influence in systems as diverse as the WWW PageRank, trophic levels of ecosystems, upstreamness of industrial sectors in complex economies, and centrality measures of social networks, as long as the underlying network is not exceedingly sparse. We also discuss the implications of this "emerging locality" on the approximate calculation of non-linear network observables.
