TAILIEUCHUNG - Managing and Mining Graph Data part 60

Managing and Mining Graph Data part 60 is a comprehensive survey book in graph data analytics. It contains extensive surveys on 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 leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. . | 580 MANAGING AND MINING GRAPH DATA 115 Yim X. Mehan M. Huang Y Waterman. M. Yu P. and Zhou X. 20007 . A graph-based approach to systematically reconstruct human transcriptional regulatory modules. Bioinformatics 23 13 i577. 116 You C. H. 1 01101 L. B. and Cook D. J. 2006 . Application of graphkiaseiS data seining metabolic pathways. Data Mining Workshops International Conferen ce on 0 169-173. 117 Zak M. 2005 . lifiirt-iernllyr mining frequent trees in a forest Algorithms and applications. IEEE Transactions on Knowledge and Data Engineering 17 8 11021 1035. 118 Zhang K. ami .liang. T. 1994 . Some MAX SNP-hard results concerning unosdered labeled trees. Infomation Processing Letters 49 5 249-254. 119 Zhang IG and Shasha. D. 1989 . Simple fast algorithms for the editing distance between isss and related problems. SIAM journal on computing . 120 Zhang S nnd Want-. T. 2008 . Discovering Frequent Agreement Subtrees from Phylogenetic Data. IEEE Transactions on Knowledge and Data Engineering 2 1 l 68-82. Chapter 19 TRENDS IN CHEMICAL GRAPH DATA MINING Nikil Wale Computer Science Engineering University of Minnesota Twin Cities US nwale@ Xia Ning Computer Science Engineering University of Minnesota Twin Cities US xning@ Gearge Karypis Computer Science Engineering University of Minnesota Twin Cities US karypis@ Abstract Mining chemical compounds in silico lias drawn increasing attention from both academia and pharmachtilictti inditnlry due to its effectiveness in aiding the drug discovery process. Since irt tiplip are tile natural representation for chemical compounds most on she mining algorithms locus on mining chemical graphs. Chemical graph mining approacltee have many applications in the drug discovery procese that include 1ationship iSAR model construction and biiitictivlly claisilicaiioUi similar compound tearch and retrieval from chemical compound daitibanCi tergni identification from phenotypic assays

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