Managing and Mining Graph Data part 50 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. . | 4778 MANAGING AND MINING GRAPH DATA I or 1 meaning7td notion f distance between nodes in the click graph Mei et al. suggest to use the notion of hitting ti me. The hitting tune from a no de u to a node v in a graph G is the expected number of steps taken when v is vitiied for a ftxst time in a random walk starting from u. Hitting time caxtures not only nodes that aee connected by h lioi r caths in the graph but also nodes that are connected by many paths. Therefore it is a robust disiancc measure between graph nodes. In addition Mei ct aL 44 propoec an adaptation of their method that can provide personatized query suggestions. The idta is to adjust the weights of the edget of the click graph so that shey can better model the preferences of the usea i ar whom we want to provide a recommendation. Mei et al. observe thar models for personatized web starch pcovtde estimates of a probability that a ttacr dicky ox a ccsiain document. Thut. any personalized algorithm for web tcarch can he combined with their method in order to provide pctsonalizcd recommendations. Topical query decomposition. A dilfontcti. aepcct of query recommendation is addrasted by Bonchi ct aL lt4- t who try to overcome a common limitation of mana query recommendation aigorithmsI This limitation is that many of the tccommcadatioas ate very siimiar trt each other. Instead Bonchi et al. for-mufate a new problem. which they call topical query decomposition. In tins new framework lite goal is to iitd o sei id queries that cover different as-pt ct of the orig na quety. Thu isllntion is that such a set of diverse queries can be rnoie utciul in cases when lhe qtrs rit is too short and thus imprecise and ambiguoust and it ii haed io receive good recommendations based on the tsutitti irotitent only. The problem ttatcinesti. of 1x11 00-1 query decomposition is based again on the click craph. in particular let q be a query and D q he She retult set of q Lea t tu nctahbor nodes of q in rhe click .
