Managing and Mining Graph Data part 45 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 Privacy-Preservation of Graphs and Social Networks 427 privacy oí7 arbitrary users. The adverstmes can adopt a hybrid semi-passive attack Uic yt create no new accounts but simp y create a few additional out-links to target users before the anonymized network is released. We refer readers to 24 for more dettiitr on theoretical resutts and empirical evaluations on a reai loctal network with inillii n todcs and 77 million edges extracted from . Structural Queries In 19 Hay ttt at. studied thicc typas of ackground knowledge to be used by acifirarstn ittf to attack naively-anonymized networks. They modeled adversaries external as the acccrs to tt source that provides answers to a ssstricted knowledge query Q about v ringie .arget norlrt in the original graph. SpedStcally Vackgrousd knowledge of adversaries is modeled using the following three types of queries. Vertex refinement queries. These aucrics dasr ttn lhe local structure of the graph around a node in an ratxre refinement way. The weakest knowlcdrc query. H0 x niiisyoitt returns tOc tabal of the no de x H1 x returns the degree of x H2 x returns rhe mrCtiiet of each neighbors degree and Hi x cvn be i cci irrit cin defined as Hi x Hi-i zi Hi-i z2 Hi-i zdx where z1 zdx arc ttre nodes adjacent to x. Subgraph que ríes. These querier can anscrl the exiftence of a subgraph around the targer node. TIit descriptive power of a query is measured by counting rhe oumbnr of edger in the described subgraph. The adversary is capable of ga hcríng some fixed number of edges focused around the taaget x. By exploring S if neighborhood of x the advrrsary teams the exórtcncf of tr subtraph around x aepresmting pcrtia information about the tc aroun d x. Hub fingerprint queries. At hub is a node in a network with high degree atd hirtfi betweenness ccnlrality. A huh fi for a target node x Ti x is a r i ciicr eieon rtf the node A conncctioos to a set of designated hubr it .
