tailieunhanh - Managing and Mining Graph Data part 3

Managing and Mining Graph Data part 3 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. . | xxiv MANAGING AND MINING GRAPH DATA matching indexing classificatiitn. cliirieting. and dense graph many cases the problem of graph management and mining has been studied from the perspective of nlruclurcd and XML data. Where possible we have clarified the connections with the methods and algorithms designed by the XML data management community. We alno . istithtic a detailed discussion of the application of graph ininlag algorithms in a number of recent applications such as graph pm vacy. web and social networks. Many af the graph algorithms are sensitive to the application scenario in avhich they are encouniereda Therefore we will study the usage of many of Shese techniques ln ttsal scenarios such as the web. social networks and bio-logicaS data. Titas poovidei a better undcrslanding of how the algorithms in the hook appiy io different rcenarios. Thus. book provides a comprehensive suminary both from an rlgorithmic and applied perspective. Chapter 1 AN INTRODUCTION TO GRAPH DATA Chani 2. Aggarwal IBM T. J. Watson Research Center Hawthorne NY 10532 charu@ Ifaixun Wang Microsoft Research Asia Beijing China 100190 haixunw@ Abstract Graph mininsa and mauegciisens has become an important topic of research re- cently because of mimarosis a epllcalions to a wide variety of data mining problems in computational bioSogy cliomicai dala analysis. drug discovery and communication networking. Traditional data mining and management algorithms s cli as cl ns leri ng i Saf ei icalioiSs fiequene pattern mining and indexing have now been extended tn the graph scenario. This bi so ie contains a number of chapters which nee- aarefuiiy chosen in i relc eo discuss site broad research issues in graph manacement and mining. Sn tidciiliiiic a number of important applications of gnaph mining are- also covered in llie booki Tine purpose of this chapter is to provide an ooenview of lite dii iereni kinsls of graph processing and mining tech-mqiieSi and

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