Có 20+ tài liệu thuộc chủ đề "sinh học tập hợp dữ liệu"
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Similar to the cascade SVM method, the representation of each compound in the training set for the 𝐿 2 models con- sists of its descriptor-space based representation and its output from each of the 𝑁 𝐿 1 models. Finally, a ranking model 𝑊 learned using the ranking per- ceptron described in the previous section. Since the 𝐿 2 model is...
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Managing and Mining Graph Data. An Introduction to Graph Data 1. Graph Data Management and Mining: A Survey of Algorithms and Applications 13 Charu C. Graph Data Management Algorithms 16. 2.3 Graph Matching 21. 3.2 Clustering Algorithms for Graph Data 32. 3.3 Classification Algorithms for Graph Data 37. vi MANAGING AND MINING GRAPH DATA 2.1 Power Laws and Heavy-Tailed Distributions...
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3.1 Approaches based on Descriptors 588. 3.2 Approaches based on Graph Kernels 589. 4.1 Methods Based on Direct Similarity 591 4.2 Methods Based on Indirect Similarity 592 4.3 Performance of Indirect Similarity Methods 594 5. 3.3 Weight properties of the campaign donations graph: (a) shows all weight properties, including the densification power law and WPL. time, that at this same...
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We also provide a detailed discussion of the application of graph mining algorithms in a number of recent applications such as graph privacy, web and social networks.. Many of the graph algorithms are sensitive to the application scenario in which they are encountered. Therefore, we will study the usage of many of these techniques in real scenarios such as the...
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10 MANAGING AND MINING GRAPH DATA [5] J. Thirty years of graph matching in pattern recognition. Faloutsos, On Power Law Relationships of the Internet Topology. An Introduction to Graph Data 11 [22] S. GRAPH DATA MANAGEMENT AND MINING: A SURVEY OF ALGORITHMS AND APPLICATIONS. Correspondingly, the applications have different requirements for the underlying mining algorithms. In this chapter, we will...
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disjoint chains, and then use chains to cover the graph. The chain-cover approach achieves 𝑂(𝑛𝑘) query time, where 𝑘 is the number of chains in the graph. 𝐷 𝑤 ∣ is the cost (size) of the 2-hop cluster centered at 𝑤. Reachability queries are one of the most basic building blocks for many advanced graph operations, and some are directly...
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One of the key challenges which arises in the context of all frequent pat- tern mining algorithms is the massive number of patterns which can be mined from the underlying database. This problem is particularly acute in the case of graphs since the size of the output can be extremely large. A pattern set 𝑃 is said to be 𝛽-representative,...
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This is a natural consequence of the fact that much of the web and social media is a relatively recent phenomenon for which new applications continue to be found over time. This law states that the number of nodes in the network increases superlinearly with the number of nodes over time, whereas the number of edges increases super- linearly over...
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It has been shown in [187] that the eigenstructure of the adjacency matrix can be directly related to the threshold for an epidemic.. Many of these techniques can also be used for other kinds of networks such as communication networks.. Structural analysis and robustness of communication networks is highly de- pendent upon the design of the underlying network graph. Careful...
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In SSWS Conference, 2005.. Query Language and Access Methods for Graph Databases, appears as a chapter in Managing and Mining Graph Data, ed.. He, Querying and mining graph databases. SIGMOD Conference, 2007.. KDD Conference, 2004.. VLDB Conference, 2002.. ACM KDD Conference, 2005.. SIGMOD Conference, 2009.. VLDB Conference, 2005.. In SIGMOD Conference, June 2002.. KDD Conference, pp. ACM KDD Confer- ence,...
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𝑁 Number of nodes in the graph 𝐸 Number of edges in the graph 𝑘 Degree for some node. Average degree of nodes in the graph 𝐶𝐶 Clustering coefficient of the graph 𝐶𝐶(𝑘) Clustering coefficient of degree-𝑘 nodes. 𝛾 Power law exponent: 𝑦(𝑥. Our focus is on combining sources from all the different fields, to gain a coherent picture of...
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The first pattern we observe is the Weight Power Law (WPL). They, they follow a power law. In other words, the more edges that are added to the graph, superlinearly more weight is added to the graph. If a node 𝑖 has out-degree 𝑜𝑢𝑡 𝑖 , its out-weight 𝑜𝑢𝑡𝑤 𝑖 exhibits a “fortification effect”– there will be a power-law relationship...
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Given a degree distribution, we can randomly assign a degree to each node of the graph so as to match the given distribution. These differ only in the degree distributions used. the rest of the graph- generation process remains the same. The PLRG model One of the obvious modifications to the Erd-os-R«enyi model is to change the degree distribution from...
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This requires 𝑂(1) time for each iteration, and 𝑂(𝑁 ) time to generate the entire graph. This technique can be easily extended to the case when the preferential at- tachment equation involves a constant 𝛽, such as 𝑃 (𝑣. 1 as in the AB model), we can handle this easily by adding ∣ 𝛽 ∣ entries for every existing node...
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A step in this direction is the Kronecker graph generator [57], which general- izes the R-MAT model and can match several interesting patterns such as the Densification Power Law and the shrinking diameters effect in addition to all the patterns that R-MAT matches.. The R-MAT genera- tor described in the previous paragraphs achieves its power mainly via a form of...
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In Con- ference of the ACM Special Interest Group on Knowledge Discovery and Data Mining, New York, NY, 2005. In Conference of the ACM Special Interest Group on Data Communications (SIGCOMM), pages 18–34, New York, NY, 2000. In Conference of the ACM Special Interest Group on Knowledge Discovery and Data Mining, New York, NY, 2002. Dynamical and correlation properties of...
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Next, we define graph patterns and graph pattern matching. We then present a graph algebra and its bulk operators which is the core of the graph query language. Finally, we illustrate the syntax of the graph query language through an example.. Tuples are annotated to the graph structures so that the representations of attributes and structures are clearly separate. Figure...
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can be mapped to 𝑣 by considering their edges. In addition, adjacency lists of the graph pattern are used to support line 21. For line 22, edges of graph 𝐺 can be represented in a hashtable where keys are pairs of the end points. To avoid repeated evaluation of edge predicates (line 22), another hashtable can be used to store...
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achieved by careful tuning and other optimizations, the results show that query processing in the graph domain has clear advantages.. Edges in the queries are matched to either edges or paths in the data graphs.. In the category of object-oriented databases, GOOD [16] is a graph-oriented object data model. GOOD models an object database instance by a directed la- beled...
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It is inefficient to perform a sequential scan on a graph database and check each graph to find answers to a query graph.. Therefore, high performance graph indexing is needed to quickly prune graphs that obviously violate the query requirement.. Shokoufandeh et al. Instead of casting a graph to a vector form, Berretti et al. The SUBDUE system developed by...