摘要: 将原始图中节点分配到多个分组并根据原始边来确立分组间关系,这样得到的图称作汇总图。汇总图的规模可以由用户设定,用户可以通过浏览小规模的汇总图来获得原始图的相关信息。K-SGS方法是一种新的基于节点概念分层的图汇总算法,它解决了传统K-SNAP算法的汇总图规模参数受限问题。为了解决该问题,算法引入了节点的属性值概念分层,从而增强了图汇总过程中节点分组的灵活性:不仅可以合并同值的节点,还可合并具有相似值的节点。除了关注汇总过程中边的信息损失外,K-SGS方法还关注节点的信息损失,它将图汇总问题建模成多目标规划问题,并通过分层序列法和基于分级的统一评价函数两种不同策略来解决该问题。算法上,提出了快速的层次聚类方法,使得每轮可以合并多个聚类,从而提高效率。经数据集上的实验表明,新算法能生产各种规模参数的汇总图,并具有较好的汇总质量。
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