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Graph structured hierarchical documents help to overcome the problems of a small screen size.
This graph structured network allows us to explicitly model interactions between topics.
Consequently, efficient knowledge discovery algorithms for mining frequent subgraphs from these dynamic streaming graph structured data are in demand.
This large graph structured data cannot be processed without using distributed algorithms due to memory constraints of one machine and also the need to achieve high performance.
The same section also discusses the important graph topological or graph structured features and the hierarchical protein classification algorithm.
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Quill starts by importing data (tables, lists, graphs) structured by other software.
Simulated pitchfork bifurcations with different graph structures.
To build this metamodel, we use a graph structure.
We also propose a self-adaptive graph structure.
The graph structure is shown in Fig. 11 a.
The extracted information is then saved in the graph structure.
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