In many modern applications, the generated data is a dynamic network. These networks are graphs that change over time by a sequence of update operations (node addition, node deletion, edge addition, edge deletion, and edge weight change). In such networks, it is inefficient to compute from scratch the solution of a data mining/machine learning task, after any update operation. Therefore in recent years, several so‐called dynamical algorithms have been proposed that update the solution, instead of computing it from scratch. In this paper, first we formulate this emerging setting and discuss its high‐level algorithmic aspects. Then, we review state of the art dynamical algorithms proposed for several data mining and machine learning tasks, including frequent pattern discovery, betweenness/closeness/PageRank centralities, clustering, classification, and regression.

中文翻译：

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用于动态图的数据挖掘和机器学习的动态算法

在许多现代应用中，生成的数据是动态网络。这些网络是通过一系列更新操作（节点添加，节点删除，边缘添加，边缘删除和边缘权重更改）随时间变化的图。在这样的网络中，在进行任何更新操作之后，从头开始计算数据挖掘/机器学习任务的解决方案效率很低。因此，近年来，提出了几种所谓的动态算法来更新解决方案，而不是从头开始计算。在本文中，我们首先拟定这种新兴的设置，并讨论其高级算法方面。然后，我们回顾针对几种数据挖掘和机器学习任务提出的最新动态算法，包括频繁的模式发现，介于中间/接近/ PageRank中心，聚类，分类和回归。