tailieunhanh - Managing and Mining Graph Data part 10

Managing and Mining Graph Data part 10 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. . | Graph Mining Laws and Generators 71 Symbol Description N E k k CC CC k 7 t Number of nodes in the graph Number of edges in the graph Degbee for some node Average degree of nodes in the graph Clustcring coeffigient of the graph Clustering coefficient of degree-k nodes Power law ent y x rc x-7 Time iterationr since the sSart of an algorithm Table . Table of symbols ideas. Our focus is on combining sources from all the different fields to gain a col-crcnt picture of the current state-of-the-art. The interested reader is also referred th some excellent and cutt-rtoiiniug books on the topic 12 81 35 . The orgiinioution of ihis chapter is as follows. In section 2 we discuss graph patterns that eppear to be common in real-world graphs. Then in section 3 we describe some graph geneeatoes which toy to match one or more of these patterns. Typically we only provide the matn ideas and approaches the interested reader can read lhe relevont references tor d m . In all of these we attempt to coitate intormation from st .p cral fields of rerearcP. Table lists the symbols we wtIt use. 2. Graph Patterns Wha- are ihe dioOinguirhing characterietics of graphs What rules and patterns hotd for them When can we say that two different graphs are similar to each other- tn order to come up with models to generate graphs we need some way of comparing a nalural graph -o a synthetically generated one the better the match the bettec the modek However to answer these questions we need to have some basic set of graph at lobate si these would be our vocabulary in whoch we can dtstuss different graph iypeOi Finding such attributes will be the tocot of this section. Whvt is a good pattern S One Ohat con help distinguish between an actual rca. iWorid graph and any fake one. However we immediately run into several problems. 10 11 8 given the c-s-Ohora oI different natural and man-made phenomena which give rite -o j- iti hit can we expect all such graphs to follow any parttculat pattern .