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Individual and collective graph mining : principles, algorithms, and applications
Koutra D., Faloutsos C., Morgan&Claypool Publishers, San Rafael, CA, 2018. 206 pp. Type: Book (978-1-681730-39-4)
Date Reviewed: Feb 11 2019

Having recently finished my course on web intelligence, with topics such as knowledge graphs, social network analysis, and web mining, as well as supervising a PhD candidate on graph-based querying and pattern matching for linked open data, this book could not have come along at a better time.

The book, however, is about graph mining approaches in that fast and principled methods for the exploratory analysis of big graphs or networks are discussed. The main incentive has been harnessing real-world graphs, which often span hundreds of millions of nodes and interactions between them, in terms of, for instance, finding the most important structures and summarizing the graphs. The main directions of these graph mining approaches have been (1) graph summarization and (2) graph similarity enabling the discovery of clusters of nodes or graphs with related properties.

The chapters are organized via the allocation of research problems into two categories: individual graph mining and collective graph mining. The latter is defined as the exploration of multiple graphs collectively, such as dynamic or temporal graphs, as well as graphs/networks from different resources. Specific research problems for each of these two categories are discussed: graph summarization (both categories), graph mining inference (first category), and graph similarity and alignment (second category).

While reading this book it quickly became apparent that it is too heavy for undergraduates working on a computer science degree. It is likely more appropriate for postgraduate students, particularly those pursuing graph mining related research problems. This is mainly due to two facts: (1) the authors primarily discuss their own approaches to graph summarization and graph similarity/alignment, and (2) advanced mathematical knowledge is required to keep up with the content.

Nonetheless, the related work discussed at the end of each chapter provides an excellent overview for beginning research and PhD work in graph similarity and alignment, as well as in graph summarization and inference (chapter 3). The latter provides interesting insights into the phenomenon of node affinity and network effects as one of the side effects of graph summarization and extraction of semantically meaningful structures, which in turn may explain connectivity at a microscopic level. For instance, we may be in a position to infer the class membership of certain nodes based on the influence of their neighbor nodes. This kind of reference, which is mentioned by the authors as a transductive inference rather than inductive, has been one of the key insights to the best of my knowledge.

Another stronghold of the book is the publicly available research code and supplementary material, for example, slides for the methods presented. Although most of the research code is written in MATLAB or Python, it is surely a good resource for those students keen on software engineering or programming.

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Reviewer:  Epaminondas Kapetanios Review #: CR146428 (1904-0096)
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