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. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.network. It has been shown in  that the eigenstructure of the adjacency matrix can be directly related to the threshold for an ... for flow dissemination in social networks can be used to model viral transmission in communication networks as well. ... The control flow of programs can be modeled in the form of call-graphs.
|Title||:||Managing and Mining Graph Data|
|Author||:||Charu C. Aggarwal, Haixun Wang|
|Publisher||:||Springer Science & Business Media - 2010-02-02|