TAILIEUCHUNG - Managing and Mining Graph Data part 38

Managing and Mining Graph Data part 38 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. . | 356 MANAGING AND MINING GRAPH DATA Figure . Top 20 discriminative subgraphs from the CPDB dataset. Each subgraph is shown with the corresponding weight and ordered by the absolute value from the top left to the bottom right. H atom is omitted and C atom is represented as a dot for simplicity. Aromatic bonds appeared in an open form are displayed by the combination of dashed and solid lines. accumulated in the past studies. In graph boosting we employed LPboost as a mother algorilhm. It is posstble to employ other algorithms such as partial least squares I sgi cs ioi i PLS 39 and least angle regression LARS 45 . When appiied to ordinary vectoriai data partial least squares regression extracts a few orthogonal teatures and perform least squares regression in the paolected space 3hS. A PLS featuse is a linear combination of original features and ii is often the case dhst correlated dentures are summarized into a PLS Itsaturst Sometimes the subgraph featutea chosen by graph boosting is not iobnst against bootstrappitsg or other data perturbations whereas the classification accuracy is quite stablei li ls due to aSrong correlation among features corresponding to timilat subgraphs. The graph mining version of PLS gPLS 39 solves thii problem by summarizing similar subgraphs into each feature Figure . Since only one graph mining caSl is required to construct each Graph Classification 3557 Figure . Patterns obtained by gPLS. Each column corresponds to the patterns of a PLS component. feature gPLS can build the classification rule more quickly than graph boosting. In graph booiting it is necessary to set the regularization parameter A in . Typicahy its is determined ley cross validftton but there is a different approach called usgi il it path tracing . When A 0 ihe weight vector cenverges to ihe origin. As A is increared continuously the weight vector draws sc piecewise linear pfth Becnuse of this property one can track the whole path by repeating

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