Đang chuẩn bị liên kết để tải về tài liệu:
Managing and Mining Graph Data part 39

Managing and Mining Graph Data part 39 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. . | Mining Graph Patterns 367 f v E E g and l u v l f u f r where I and 1 are the labeling functions of g md g respectively. f is called an embedding ofg in g1. Definition 12.2 Frequent Graph . Given a labeled graph dataset D Gi G2 . Gn and a sub graph g ihe supporting graph set of g is Dg IG- n C G- G- P Php vr j muii i nt n iv ri riiir I 91 A vj-j g vj-j vj-j e d niL tyj g to o ufdfdui G g . ei jt c-cy 4 c-Ait- graph is a graph whose support is no less than a minimum support threshold minsup. An important property called anti-monotonicity is crucial to confine the search space of frequent subgraph mining. Definition 12.3 Anti-Monotonicity . Ami-monotonicity means that a size-k snbgraph is frequent only if all of its subgraphs are frequent. Many frequent graph pattern mining algorithms 12 6 16 20 28 32 2 14 15 221. 21 8 3 have been proposed. Holder et al. 12 developed SUBDUE i.o do appciiximat.rr graph pattern discovery a se cl on minimum description length and background knowlrdgc. Dehaspe t cl. 6 applied inductive logic progcamming to predici chemical cercinogemcity by mining frequent subgraphs nisidcs these rludies there arc two basic approaches to the frequent subgraph mining problem the Apriori-hased t iirpiair clc and tire pattern-growth approach. 2.2 Apriori-based Approach Apriori-based ircs ucnt eubgroph mining algociihms share similar character-istrss with Apriori-based ircciiicnt itemset mining argorithms. The search for frequent subgraphs ctarts with smallcslzs ruhgraphs. and proceeds in a bottom-up manner. Ac cadi Iteration. the iize of nccviy discovered frequent subgraphs is increased by oni. These new rubgraphs arc generated by joining two simi-Car hut siighiiy difieienC frequent subgraphs that were discovered already. The frequency ol the ncevly loimcd graphs in then checked. The framework of ApriorI-harcd meChods ie outlined in Algorithm 14. Typical Apriori-based trecpncnt suhgraciii mining algorithms include AGM by tnokiichi ct al. 16 FSG by Kuramocht