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Best Graph Mining Books

The AI Optify data team writes about topics that we think data scientists will love. AI Optify has affiliate partnerships so we may get a share of the revenue from your purchase.

Best Graph Mining Books - For this post, we have scraped various signals (e.g. reviews/ratings, covered topics, author influence in the field, year of publication, social media mentions etc.) from web for a number of graph mining books. We have fed all above signals to a trained Machine Learning algorithm to compute a score for each book and rank the top books.

The readers will love our list because it is Data-Driven & Objective. Enjoy the list:

1. Managing and Mining Graph Data (Advances in Database Systems)

Score: 100/100

Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing.

2. Data Mining: The Textbook

Score: 70/100

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way.

3. Practical Graph Mining with R

Score: 39/100

Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs.

4. Mining Graph Data

Score: 24/100

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets