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WIREs Data Mining Knowl Discov
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Dynamical algorithms for data mining and machine learning over dynamic graphs

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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. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Big Data Mining
An example of updating the adjacency matrix of the graph, after a node deletion
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An example of semi‐bipartite graph representation of the network. A dotted line shows the presence of a discriminative word in a graph node
[ Normal View | Magnified View ]

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Fundamental Concepts of Data and Knowledge > Big Data Mining
Technologies > Machine Learning
Technologies > Structure Discovery and Clustering

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