tailieunhanh - Managing and Mining Graph Data part 54

Managing and Mining Graph Data part 54 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. . | 520 MANAGING AND MINING GRAPH DATA needed to find discriminative atls rns in the last step. Obtaining the necessary information can lite done easiSy as quality assurance widely uses test suites which provide this correct results 18 Step 2 Caliigraph rcduc lisin is necessary So overcome the huge sizes of call graphs. This is much more ehalicngingi It involves the decision how much information lost its toierabie when compscssing the graphs. However even if reduction tcchaiqucs can laciiitate miniug in masy cases they currently do not ahow for mining of arbitrrsy software prosecSs. Details on call-graph reduction are psesented rn Section 4. Step 3 This step mntudes frequent subgraph mining and the analysis of sesu 1 frequent subgraphSt Tire intuition isc So search for patterns typical irrn executions This alien resits in a ranking of methods suspected to contain a hugt The rationale is that ssich a ranising is given to a software developer who can do a sss .I _ rcvittw of the suspicious methods. The specifics of iliis step vauy widely and highly depend on the graph-reduction scheme used. Section 5 discusfcs the different approaches in detail. 2A Graph and Tree Mining I reciucnt. subgraph mining har heen introduced in earlier chapters of this book. As such tet lmin iu i ace oi tm t ist rhis chapter we briefly reca- ltatc thoac winch arc used in llaa context oi btig localization based on call graph mining Frequent subgraph mining frec noni. subgraph mining searches for complem uei. of subgraphs which issc irequent within a database of gi aj lit i wich rcspuci is a men defined minimum support. Respective algoiiihms can mine connected graphs containing labeled nodes and cdgeSi Most implemcntationa alio handlu directed graphs and pseudo graphc which mighi coniain self-loops and multiple edges. In general graphu irnida ii i l can contain cycleSi A prominent mining algorithm is gSpan 32 Closed frequent subgraph mining Is s c .