TAILIEUCHUNG - Managing and Mining Graph Data part 29

Managing and Mining Graph Data part 29 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. . | 2664 MANAGING AND MINING GRAPH DATA nodes score edges and nodes separately and combine the scores. Specifically each edge has a pre-defined weigh. and default to 1. Given an answer tee T lor each keyword ki we me s T ki to raa ris seenl. the sum of the 011 ssi weights on dees fiath from the root of T to ihd leal containing keyword kii Thus the aggrcgaied edge score is E s T ki . The erodes on She other hards are scoacd by their grlolcsU importance or prestige which is usually based on PagcRank 4 random walk. Let N denote tins eggregated score oh nodes thas contain keyworde. The combined score of an answer tree is given by s T ENx where A isc ts ad nst irnpostance of edge and node scores 13 21 . Qucsy semantics and ranks ns tratcgics tied in BLINKS 14 are similar to those of BANKS S4 sand She etidirc ctiona sttscch 21 But instead of using a measure such as S T ENx do final lop-K answers BLINKS requires that eacii of the anievcr has as dil lcraet root node or in other words for all answas trees rooted ai the same nodCi oniy the one with the highest score is considered for top-K. Thts scmaniics guasds against the case where a hub pointing io many nodes containing query keywords becomes the root for a huge numbes of answers. These cresevers ovet ap and each carries very little addiitona tnioernaiSon from the rest. Gives sra answer which is the best or one of lhe besO aid fit rootf urois san a wayt choose to further examine other answers wifli this root 14 Unfike most keyword search on graph data approaches 3 21 14 Objec-dRank 2 does noi return anawcr teis or ruhgsaphs containing keywords in the s lK. i il. inetcad. itsi ObiCctRank. an answer ic simply a node that has high auihoriSy on thr keywords in the c elrry. Hence a node that does not even con-tarn a r t cis ili keyword tn the query may stH 1 qualify as an answer as long as enough on that keyword has flown into that node Imagine a node that represent a paper whSc-h does not contain .

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