tailieunhanh - Managing and Mining Graph Data part 32

Managing and Mining Graph Data part 32 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. . | A Survey of Clustering Algorithms for Graph Data 2995 is 1 A M Si k. Siiin larl let the average sub-structural self-sirmite ity at end of the the previous iteration be . In the beginning of the next. deration the algorithm confutes the increase of the average sub-structueal sei hsi mi lari ty. and c-hechs if it is smaites th an a user-specified threshold e. If nog ittc ateorithm procerds with another iteration. Otherwise the algorithm terminates tn addition an uppor bound on the number of iterations ls imposed. This ls done in otdcr iti effectively handle situations in which the ttaeshold e is fhosen to be Do imaif Two further issues need to be implemented in order tee 1 y use the underlying algorithm We need do dctetminc cllcciivc mcthodi for determining the similarity between a. given documinti and it group of other documents. Techniques bor eomputing rite rlmilarity Eire discussed in 2 We need iii dctetminc itequenl ttructural patterns in the underlying doc-umenOs. This can lie a huge challtteec in many applications especially since stauctueal data is far moec challenging to mine than transactional date. it hat been shown in I2 how sequential pattern mining algorithms can be adapted to the cate of sOnictural data. The broad idea is to flatten ou t the tree clrttclurc into a sequential pattenn by using a pre-order travcrsali Then edit ciurtertng ii perioemed on the resulting sequential patteeni. h has been shown t ehat such an approach is able to retain moel of tiic1 slrllctllral information in ihe date while introducing some spurious relationa. The overatt approach has been shown in 2 to be erx l rtlnenlteIff qutlc effective. It has been shown in 12 that. diir method Is far more effective than competing t e cj-iies suah as ihotc discussed in 10 29 4. Applications of Graph Clustering Algorithms Gaaph ciustering afgorirhms iind numcrour applications in the literature. An discutscd in chapter giaph mining atgoitihms fall into the categories if