Dynamical algorithms for data mining and machine learning over dynamic graphs

Abstract

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.