Managing and Mining Graph Data part 49 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. . | 4668 MANAGING AND MINING GRAPH DATA - Eb represents than best answers u v G Eb if user u has provided at least one best answer to a question asked by user v. - Ev represents thf votes for best answer u v G Ev if user u lias votfd foa best answee at leeis l. one answer given by user v. - Es represents iltc etats given to questions u v G Ev if user u lias given a star to at least one question asked by user v. - E E_ represents life thumbs up down u v G E E_ if user u lias liven if thumbs up down to an answer by user v. for each graph Gx V Ex hx is the vectoi of huh scores on the vertices V ax tti e xrc clxvi of authority scores and px time vcctos of PageRank t coi es. Moreover px is -the vector ot PageRank scores in the transposed graph. To cifssily tiicrc features in our framework PageRank and authority scores itee assumed to be reiated mostly to in-links while the hub score tlttals mostly with oni-tinks. For instance let us consider hb. It is hub tcore ie tha best asswce graph ir which an out-link from u to v means that u gave a heel answer to us er v. Then. hb rcpritsc tits the answers of users and is assigecil to the record UA of list poison answering the question. Content usage statisties. Usage statistict such as the number of clicks on die item end time spent on the ilitiri have been shown useful in the contef I. t t tdentiSymg hegte quatity web search results. These are com-peementary Vo llekianatytls based methodSt Intuitively usage statistics llicaeurcs ate 06 1 for social media fomente but require different interpretation from lite plcviouely studied settings. In lbe QA t ett inslel it is ejststil l to exploit the rich set of metadata avail-ahlf foa each queetion. This metuder temporal statistics . how long ll ie question wat postedt which ahotvs us to give a better interpretation to the nombcs of vtews of a quettion. Also given that clickthrough counts on a question eiie bcatri 1 y tollucnced by the topical and genre category ttte al so use .
