基于边选择的PageRank优化
PageRank Optimization by Edge Selection
摘要：
有向图中节点的重要性可以通过其PageRank来衡量。节点的PageRank用于许多应用程序上下文（包括排名网站），并且可以解释为无限随机游走在节点上花费的时间的平均部分。我们考虑的问题，最大化的PageRank的一个节点，通过选择一些边缘从一组边缘，是在我们的控制。通过应用马尔可夫决策理论的结果，我们表明，这个问题的最优解可以找到在多项式时间。我们的核心解决方案的结果在一个线性规划公式，但我们也提供了一个替代的贪心算法，一个变种的政策迭代，它运行在多项式时间，以及。最后，我们表明，根据轻微的修改，我们给出了互斥的边缘对，PageRank优化的问题成为NP难。
英文摘要：
The importance of a node in a directed graph can be measured by its PageRank. The PageRank of a node is used in a number of application contexts - including ranking websites - and can be interpreted as the average portion of time spent at the node by an infinite random walk. We consider the problem of maximizing the PageRank of a node by selecting some of the edges from a set of edges that are under our control. By applying results from Markov decision theory, we show that an optimal solution to this problem can be found in polynomial time. Our core solution results in a linear programming formulation, but we also provide an alternative greedy algorithm, a variant of policy iteration, which runs in polynomial time, as well. Finally, we show that, under the slight modification for which we are given mutually exclusive pairs of edges, the problem of PageRank optimization becomes NP-hard.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
