TAILIEUCHUNG - Managing and Mining Graph Data part 59

Managing and Mining Graph Data part 59 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. . | 570 MANAGING AND MINING GRAPH DATA from the perspective mining of mining a singin large network in the presence nf noise and uncertainty. Both data mining and the tick of hioiniormaiics are young and vibrant and thus there tare ample opportunities for fnteresting tines of future research at their iptcrecction. Sticking ter iheme of oCiln article - graph mining in biomformatics - bnlow we list sivcral such opportunities. This list is by no means a compachcnoiora till het I highiighi. eomc of the potential opportunities icscarchcrs mny avail of. Scalable algctr itlmis for aaalyzing time varying networks A large ma-jititilet of die evo-C to date in field has focused on the analysis of static network. OVIiila laavc feen soma rcocnl efforts to analyze dynamic biological networkCi research in Shis arena iii at its infancy. With anticipated advances iir tcchnoloay where much more temporal data is likely io become avatiahiia temporal analysis of such networks is likely to be an importani nrena ok future resevreh. Underpinning this effort given the size and dynamics of the data nvolved aac rhe need to develop scalable aigoolthms for pcocersing and onalyzing such data. Discovering anomalous slructurcf in graili data Again while most of ihe work han incused on iltn discovery of frequent or modular structure within such data - the diicovety of anomalous substructures of ico lias a crucial role to piay in such domains. Defining what constitutes an anomaly how hr compute i clliciently while leveraging the ambient knowledge in domain in question are some of the challenges io be addressed. Integrating data from multiple possibly conllicting sources A fundamental chaiicngc in bioiniorlllalica ic gcncrai is that of data integration. Data ic t i aikihikt in many formáis anil often times are in conflict. For example protein interaction data producid by various experimental methods imass o ccti oinctiiy. Ycaat2Hybridi m-rilico are often in conflict.

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