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Managing and Mining Graph Data part 47

Managing and Mining Graph Data part 47 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 4447 social graph. It considered the case where no underlying graph is released and. in fact. -tic owner ol the network would like to keep the entire structure ol the p rc i oln hidden from any one. The goal of the adversary is rather than to dc-anonymizc parllculor indivlduais from thai graph to compromise the link privacy ol ae many individuals as pocsihlr. Specifically the adversary determines the Iirtfi structure of the graph based on the local neighborhood views of the graph from rhe perspective of several non-anonymous users. Anaiysis showed that the number of users that need to be compromised in osder to coves a con-tone l-action of i.hc entire network drops exponentially we th increase in the iooCahcad param eter I provided hy thh netsvork data owner. Here t network has a lookahead I ii a rcglstcicd -tier can ree ail the links and noder inc denS to him svithin distance I horn him. For example I 0 ii a user can. sen xe tctly who he links to I 1 ii a user con sep exac-iy the friends that tic -inks to es wcIl t-h the friend. l.tiat his friends link to. Each time the adversyry sains. acccis to st user account he immediately covers all nod s Usai arc at dlcta-cc no more than the lookahead di stance I cnahlcd by die so-i. al ne-worki In other worsir lie Ica-m about all the edges incident to liie-c nodrs. Thus irj. gaming acoesr to the account of user u an adversary hiimcdialciy covers all nod-s thal are within distance I of u. Addttlonally. he ice-ins about the existence o- ait nodes within distance I 1 irom u. The authors rlndiccl several atlacking -trai.cisies shown as below. Bcnchmark-Grcndy Among ail users in the social network pick the nc.xl utcr t Iribe at Ihe one whose pcnspcctivc on the network gives the iargert possiiclc amount of new iiilormailon. Formally at each step the advnrsary picks the rode covering the maximum number of nodes not .it ririlih. Heuristicaiiy Greedy- Pick the next user