Dynamical algorithms for data mining and machine learning over dynamic graphs

In many modern applications, the generated data is a dynamic networks. The 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 these 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. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Big Data Mining