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Managing and Mining Graph Data part 8

Managing and Mining Graph Data part 8 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. . | Graph Data Management and Mining A Survey of Algorithms and Applications 51 network. It lias been shown in 187 that the cigcnstructure of the adjacency matrix can lit directly related to the threshold for an epidemic. Other Computer Network Applications. Many of thise techniques can at be used for other kt nds of networks ruch as communication networks. Structural anatysis and robustness of communication networks is highly de-pendenb upon the skusistn of rise unsVerlymg network graph. Careful design of rhe underlyisg graph can tiety rivoict nelwosk failures congestions or other wcakscsscs in ihe overtill network For exampie centrality analysis 158 can be used in the nontext of a communication network in order to determine critical pointe of taitsirc. S-milarly. the tcs-hnlrtsics for flow dissemination in social networks can he used to model viral iransmitsion in communication networks as well- The mem dilleoettca it that we model viral infection probability along an odsc in i communication neSstttt tS instead of the information flow probability along an edge in o social network. Many reachebility techniques 10 48 49- 53t 54 184 can be used to determine optlmat rt tiling decisions in computer networks. This is also related lo She protlcm of detcr-mning pairwise node-connectivity 7 in computer networks. The mtshr irtrsi in t 71 utes s cc tne r sekin-based synopsis to create an effective connfc-ivity index tor massive slisk-rcaident graphs. This is useful in communication nel-worte in winch we nbed to determine the minimum number nf edges to bt deteted in ordes tee rllsconncttr. a particular pair of nodes from one another. 4.3 Software Bug Localization A naiurai astbltcOLt.ion aO graph minlnt atgorithms is that of software bug iocaltzationt Software big locnlizalion ie in meportant application from the pesspective of software refiabflity and lcsl.mg. The control flow of programs cm he moslc-ctl in iliit form of can-graphti The goal of software bug localization .