managing and sharing customer data

... biomolecular target’s chemical data analy- sis. In recent years, the trend has been to integrate chemical data with protein and genetic data (bioinformatics data) and analyze the problem over multiple proteins ... Graph Data Mining 601 dustry has generated a wealth of protein-ligand activity data for large com- pound libraries against many biomolecular targets. The data has been system- atically collected and ... Classification, 40 XML Clustering, 35, 291 XML Indexing, 4, 17 602 MANAGING AND MINING GRAPH DATA sent interactions between drugs and targets, and then used kernel regression to the relationship among...

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... 1 2. Graph Management and Mining Applications 3 3. Summary 8 References 9 2 Graph Data Management and Mining: A Survey of Algorithms and Applications 13 Charu C. Aggarwal and Haixun Wang 1. Introduction ... Conclusions and Future Research 55 References 55 3 Graph Mining: Laws and Generators 69 Deepayan Chakrabarti, Christos Faloutsos and Mary McGlohon 1. Introduction 70 2. Graph Patterns 71 x MANAGING AND ... Beijing viii MANAGING AND MINING GRAPH DATA 6. Vector Space Embeddings of Graphs via Graph Matching 235 7. Conclusions 239 References 240 8 A Survey of Algorithms for Keyword Search on Graph Data 249 Haixun...

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... sizes of the second and third-largest connected components (CC2 and CC3) stabilize. We fo- cus on these next-largest connected components in (c). 84 xx MANAGING AND MINING GRAPH DATA 17.1 An unreduced ... Eqs. (2.5) and (2.6) are 0.7810 and 0.5217, respectively. 492 16.3 A toy example (reproduced from 61) 496 16.4 Equivalence for Social Position 500 xviii MANAGING AND MINING GRAPH DATA 7.3 Graph ... superlinearly-more money it donates, and similarly, the more donations a candidate gets, the more average amount-per-donation is received. Inset plots on (c) and (d) show 𝑖𝑤 and 𝑜𝑤 versus time. Note they...

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... LLC 2010 C.C. Aggarwal and H. Wang (eds.), Managing and Mining Graph Data, Advances in Database Systems 40, DOI 10.1007/978-1-4419-6045-0_1, 6 MANAGING AND MINING GRAPH DATA In the second case, ... the web and social networks are defined on massive graphs 4 MANAGING AND MINING GRAPH DATA Natural Properties of Real Graphs and Generators. In order to under- stand the various management and mining ... in the case of structured data than in the case of multi-dimensional data. The problem of managing graph data is related to the widely stud- ied field of managing XML data. Where possible, we will...

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... graph data management and min- ing algorithms are required. This includes web data, social and computer networking, biological and chemical data, and software bug localization. 16 MANAGING AND ... algorithms and applications. 2.1 Indexing and Query Processing Techniques Existing database models and query languages, including the relational model and SQL, lack native support for advanced data ... localization and computer networking. In addition, many new kinds of data such as semi- © Springer Science+Business Media, LLC 2010 C.C. Aggarwal and H. Wang (eds.), Managing and Mining Graph Data, 13 Advances...

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... both the database and the IR communities. Graph is a general structure and it can be used to model a variety of complex data, including relational data and XML data. Because the underlying data assumes ... is to build a 24 MANAGING AND MINING GRAPH DATA [94], random walk kernels [81] and diffusion kernels [119]. In random walk kernels [81], we attempt to determine the number of random walks between the ... nodes in the graph independently and perform random walks starting from these nodes. These random walks can be Graph Data Management and Mining: A Survey of Algorithms and Applications 29 used in...

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... transition any web page in the collection uniformly at random. 50 MANAGING AND MINING GRAPH DATA examine the problem of community detection and change detection in a single framework. This provides ... relationship (SAR) princi- 46 MANAGING AND MINING GRAPH DATA Let 𝐴 be the set of edges in the graph. Let 𝜋 𝑖 denote the steady state proba- bility of node 𝑖 in a random walk, and let 𝑃 = [𝑝 𝑖𝑗 ] denote ... dissemination in the underlying Graph Data Management and Mining: A Survey of Algorithms and Applications 41 Densification: Most real networks such as the web and social networks con- tinue to become...

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... methods, procedures and functions in the program are nodes, and the relationships between the different methods are defined as edges. It is also possible to define nodes for data elements and model relationships ... graphs are created during program execution, and they represent the invocation structure. For example, a call from one pro- 56 MANAGING AND MINING GRAPH DATA [10] R. Agrawal, A. Borgida, H.V. Jagadish. ... of simple methods. 60 MANAGING AND MINING GRAPH DATA [75] M. Fiedler, C. Borgelt. Support computation for mining frequent sub- graphs in a single graph. Workshop on Mining and Learning with Graphs (MLG’07),...

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... Query Language and Access Methods for Graph Databases, appears as a chapter in Managing and Mining Graph Data, ed. Charu Aggarwal, Springer, 2010. [97] H. He, Querying and mining graph databases. ... GRAPH DATA [175] H. Tong, C. Faloutsos, J Y. Pan. Fast random walk with restart and its applications. In ICDM, pages 613–622, 2006. [176] S. TrißI, U. Leser. Fast and practical indexing and querying ... fields and harmonic functions. ICML Conference, pages 912– 919, 2003. Graph Data Management and Mining: A Survey of Algorithms and Applications 65 [159] P. R. Raw, B. Moon. PRIX: Indexing and querying...

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... of the WWW, Web “clickstream” data, sales data in retail chains, file size distributions, and phone usage data. 2.2 Small Diameters Informal description:. Travers and Milgram [80] conducted a famous ... in the graph, and sum the results to find the total 74 MANAGING AND MINING GRAPH DATA sented as a table with the schema Graph(fromnode, tonode), the code for calculating in-degree and out-degree ... [43] conjecture that for many graphs, the neighborhood size 𝑁 ℎ 80 MANAGING AND MINING GRAPH DATA graphs to random failures, and correlations found in the joint degree distri- butions of the graphs....

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... generators, we provide citations and a summary. 3.1 Random Graph Models Random graphs are generated by picking nodes under some random prob- ability distribution and then connecting them by edges. ... R « enyi in the 1960s [40, 41]. Their random graph model was the first and the simplest model for generating a graph. Description and Properties. We start with 𝑁 nodes, and for every pair of nodes, an ... point represents a node and the 𝑥 and 𝑦 coordinates are its degree and total weight, respectively. To achieve a good fit, we bucketize the 𝑥 axis with logarithmic binning [64], and, for each bin, we...

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... random and preferential attachment Instead of pure prefer- ential attachment, the endpoints of new edges are chosen according to a linear combination of preferential attachment and uniform random ... at time 94 MANAGING AND MINING GRAPH DATA where 𝑘(𝑖) is the degree of node 𝑖. Note that since the generated network is undirected, we do not need to distinguish between out-degrees and in-degrees. The ... these edges is given by 𝑃 (edge to existing vertex 𝑣) = 𝑘(𝑣) ∑ 𝑖 𝑘(𝑖) (3.14) 100 MANAGING AND MINING GRAPH DATA 𝑡, and 𝛼 ∈ [0, 1] is a free parameter. To rephrase the equation, in order to choose...

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... 104 MANAGING AND MINING GRAPH DATA where 𝑑 𝑖𝑗 is the distance between nodes 𝑖 and 𝑗, ℎ 𝑗 is some measure of the “centrality” of node 𝑗, and 𝛼 is a constant that controls ... devastating. 110 MANAGING AND MINING GRAPH DATA The recursive nature of the partitions means that we automatically get sub-communities within existing communities (say, “RedHat” and “Mandrake” enthusiasts ... parameters as possible. There should be a fast parameter-fitting algorithm. 102 MANAGING AND MINING GRAPH DATA Description and properties:. As an example, suppose we have a for- est which is prone...

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... value of customers. In Conference of the ACM Special Interest Group on Knowl- edge Discovery and Data Mining, New York, NY, 2001. ACM Press. [35] Sergey N. Dorogovtsev and Jos « e Fernando Mendes. ... de Wet, and Yuri Goegebeur. A goodness-of-fit statistic for Pareto-type behaviour. Journal of Computational and Applied Mathematics, 186(1):99–116, 2005. 116 MANAGING AND MINING GRAPH DATA Small ... it only to differentiate between exponential and sub-exponential growth 120 MANAGING AND MINING GRAPH DATA [42] Alex Fabrikant, Elias Koutsoupias, and Christos H. Papadimitriou. Heuristically...

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... 2010 C.C. Aggarwal and H. Wang (eds.), Managing and Mining Graph Data, Advances in Database Systems 40, DOI 10.1007/978-1-4419-6045-0_4, 125 128 MANAGING AND MINING GRAPH DATA P A B A 1 B 1 C 1 B 2 G C ... V1.vid = E1.vid1 AND V1.vid = E3.vid1 AND V2.vid = E1.vid2 AND V2.vid = E2.vid1 AND V3.vid = E2.vid2 AND V3.vid = E3.vid2 AND V1.vid <> V2.vid AND V1.vid <> V3.vid AND V2.vid <> ... of terminals and nonter- minals, and a finite set of production rules. A production rule consists of a 122 MANAGING AND MINING GRAPH DATA [67] Mark E. J. Newman, Stephanie Forrest, and Justin Balthrop....

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