By Charu C. Aggarwal
Managing and Mining Graph Data is a accomplished survey ebook in graph information analytics. It includes broad surveys on very important graph subject matters corresponding to graph languages, indexing, clustering, information iteration, development mining, category, key-phrase seek, trend matching, and privateness. It additionally reports a couple of domain-specific situations akin to circulation mining, net graphs, social networks, chemical and organic info. The chapters are written through top researchers, and supply a extensive point of view of the realm. this is often the 1st finished survey e-book within the rising subject of graph info processing.
Managing and Mining Graph Data is designed for a diversified viewers composed of professors, researchers and practitioners in undefined. This quantity is usually compatible as a reference ebook for advanced-level database scholars in computing device science.
About the Editors:
Charu C. Aggarwal acquired his B.Tech in laptop technological know-how from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has labored as a researcher at IBM considering the fact that then, and has released over one hundred thirty papers in significant facts mining meetings and journals. He has utilized for or been granted over 70 US and foreign patents, and has three times been distinct a grasp Inventor at IBM. He has got an IBM company award for his paintings on facts movement analytics, and an IBM remarkable Innovation Award for his paintings on privateness expertise. He has served at the govt committees of so much significant info mining meetings. He has served as an affiliate editor of the IEEE TKDE, as an affiliate editor of the ACM SIGKDD Explorations, and as an motion editor of the DMKD magazine. he's a fellow of the IEEE, and a life-member of the ACM.
Haixun Wang is presently a researcher at Microsoft examine Asia. He acquired the B.S. and the M.S. measure, either in laptop technological know-how, from Shanghai Jiao Tong college in 1994 and 1996. He got the Ph.D. measure in desktop technology from the college of California, l. a. in 2000. He therefore labored as a researcher at IBM until eventually 2009. His major examine curiosity is database language and structures, info mining, and knowledge retrieval. He has released greater than a hundred examine papers in referred foreign journals and convention complaints. He serves as an affiliate editor of the IEEE TKDE, and has served as a reviewer and application committee member of prime database meetings and journals.
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Additional info for Managing and Mining Graph Data
Sample text
Graph streams are very challenging to mine, because the structure of the graph needs to be mined in real time. Therefore, a typical approach is to construct a synopsis from the graph stream, and leverage it for the purpose of structural analysis. It has been shown in [73] how to summarize the graph in such a way that the underlying distances are preserved. Therefore, this summarization can be used for distance-based applications such as the shortest path problem. A second application which has been studied in the context of graph streams is that of graph matching [140].
In each category, we briefly discuss query semantics, ranking strategies, and representative algorithms. Keyword search over XML data. XML data is mostly tree structured, where each node only has a single incoming path. This property has significant impact on query semantics and answer ranking, and it also provides great optimization opportunities in algorithm design [197]. Given a query, which contains a set of keywords, the search algorithm returns snippets of an XML document that are most relevant to the keywords.
Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography. WWW Conference, 2007. [18] T. Kudo, E. Maeda, Y. Matsumoto. An Application of Boosting to Graph Classification, NIPS Conf. 2004. [19] J. Leskovec, J. Kleinberg, C. Faloutsos. Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 1(1), 2007. [20] K. Liu and E. Terzi. Towards identity anonymization on graphs. ACM SIGMOD Conference 2008.