LiveRank：如何刷新旧数据集
LiveRank: How to Refresh Old Datasets
摘要：
本文考虑了数据集的刷新问题。更准确地说，给定一个节点集合（网页、来自在线社交网络的用户）以及一些结构（超链接、社交关系），我们希望识别出当前仍然存在的节点的重要部分。旧节点的活性可以通过当前的在线查询来测试。我们将LiveRank称为旧页面的排名，以便活动节点更有可能首先出现。LiveRank的质量是通过使用LiveRank顺序时识别给定部分活动节点所需的查询数量来衡量的。我们研究了不同的场景，从静态设置，在任何查询之前计算的Liv-eRank，到动态设置，在处理查询时可以更新LiveRank。我们的研究结果表明，建立在PageRank可以导致有效的LiveRanks，Web图形以及在线社交网络。
英文摘要：
This paper considers the problem of refreshing a dataset. More precisely , given a collection of nodes gathered at some time (Web pages, users from an online social network) along with some structure (hyperlinks, social relationships), we want to identify a significant fraction of the nodes that still exist at present time. The liveness of an old node can be tested through an online query at present time. We call LiveRank a ranking of the old pages so that active nodes are more likely to appear first. The quality of a LiveRank is measured by the number of queries necessary to identify a given fraction of the active nodes when using the LiveRank order. We study different scenarios from a static setting where the Liv-eRank is computed before any query is made, to dynamic settings where the LiveRank can be updated as queries are processed. Our results show that building on the PageRank can lead to efficient LiveRanks, for Web graphs as well as for online social networks.
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。
