TAILIEUCHUNG - Managing and Mining Graph Data part 58

Managing and Mining Graph Data part 58 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. . | 560 MANAGING AND MINING GRAPH DATA Two ol ths Ufological structures discovered with their toolkit are depicted in r igurc . Such large structures cannot he obtained by using standard motif minings algorithms. As noted by the authors itic- identified topological structures arc maiciy composed of polar N T S. charged K and aromatic W tesiduts which is in agreement with biophysics literature. Figure . Frequent Topological Structures Discovered by TSMiner Motif Discovery in Biological Networks In addition to .ilagrifilts that ace trtic sietst. across many networks substructures that are 00 0101x1 ircciuynily wiShin a single and large network can be useful loo knowledge diiicovcry. A motif of a graph refers to a substructure which is ici i ars considerably inside the graph. There are two main approaches Crequencyibasid and tica i i to detemrine rhe oignificancc of this repetition. The frequevey-based acpcoach concidcih c subgraph as a motif if it is occurring more than rs cshoid nimthch of iiuic i On the other hand statistical approach labels a subgraph as motif it is is occuning more than the expected number of timet willt respect to random network. ihclivvork motifs can be particularly eCfective in i initcci tisnilinc the modularity end tho global structure of biological networks. Tor cxampic in ttse case of PPI networks motifs can be useful for the identification of protein complexes and other protein groupings that are related ho rhe mechanics of itic Id inca orgacism In aase of regulatory networks motifs ent-hle understmding goto regulation mechanisms and it also enables tescarchcrt to lc witi iT models and experiment. to understand these mechanics. Milo et al. is rhe first to define nctwook motiOs and find them in networks from biocCcmislryi ncuso biology ecology end engineering 78 They defined netwock motifs as patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in .

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