• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.33-39
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• 2024

In a previous study we proceeded to the remodularization architecture based on classes and packages using the Formal Concept Analysis (FCA)[13] [14] [30]. we then got two possible remodularized architectures and we explored the issue of redistributing classes of a package to other packages, we used an approach based on Oriented Graph to determine the packages that receive the redistributed classes and we evaluated the quality of a remodularized software architecture by metrics [31] [28] [29]. In this paper, we will address the issue of the efficiency of the Oriented Graph in the remodularization of software architectures compared to the Formal Concept Analysis FCA method. The formal method of FCA concept is not popularized among scientists as opposed to the use of the labeled directed graph. It is for this reason that our directed graph approach is more effective in its simplicity and popularity.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.61-67
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• 2017

This paper analyzes the robustness of the network based on the centrality of vertices in the graph. In this paper, a random graph is generated and a modified graph is constructed by adding or removing vertices or edges in the generated random graph. And then we analyze the robustness of the graph by observing changes in the centrality of the random graph and the modified graph. In the process modifying a graph, we changes some parts of the graph, which has high values of centralities, not in the whole. We study how these additional changes affect the robustness of the graph when changes occurring a group that has higher centralities than in the whole.

• Environmental and Resource Economics Review
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• v.12 no.4
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• pp.687-708
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• 2003

The purpose of this paper is to introduce time-series causality analysis by combining time-series technique with graph theory. Vector autoregressive (VAR) models can provide reasonable interpretation only when the contemporaneous variables stand in a well-defined causal order. We show that how graph theory can be applied to search for the causal structure In VAR analysis. Using Maryland crop cash prices and CBOT futures price data, we estimate a VAR model with directed acyclic graph analysis. This expands our understanding the degree of interconnectivity between the employed time-series variables.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.643-650
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• 2004

Control flow graph is used for Performing many Program-analysis techniques, such as data-flow and control-dependence analysis, and software-engineering techniques, such as program slicing and testings. For these analyses to be safe and useful, the CFG should incorporate the exception flows that are induced by exceptions. In previous research to construct control flow graph, normal flows and exception flows are computed at the same time, since these two flows are known to be mutually dependent. By investigating realistic Java programs, we found that the cases when these two flows are mutually dependent rarely happen. So, we can decouple exception flow analysis from normal flow analysis. In this paper we propose an analysis that estimates exception flows. We also propose exception flow graph to represent exception flows. And we show that the control flow graph that accounts for exception flows can be constructed by merging exception flow graph onto normal control flow graph.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.725-732
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• 2014

The Pancake graph is node symmetric and useful interconnection network in the field of data sorting algorithm. The Half Pancake graph is a new interconnection network that reduces the degree of the Pancake graph by approximately half and improves the network cost of the Pancake graph. In this paper, we analyze topological properties of the Half Pancake graph $HP_n$. Fist, we prove that $HP_n$ has maximally fault tolerance and recursive scalability. In addition, we show that in $HP_n$, there are isomorphic graphs of low-dimensional $HP_n$. Also, we propose that the Bubblesort $B_n$ can be embedded into Half Pancake $HP_n$ with dilation 5, expansion 1. These results mean that various algorithms designed for the Pancake graph and the Bubble sort graph can be executed on $HP_n$ efficiently.

• 전기의세계
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• v.17 no.2
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• pp.11-15
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• 1968

One of the most important methods used in the modern analysis of linear networks and systems is the signal flow graph technique, first introduced by S.J. Mason in 1953. In essence, the signal-flow graph technique is a graphical method of solving a set of simultaneous. It can, therefore, be regarded as an alternative to the substitution method or the conventional matrix method. Since a flow-graph is the pictorial representation of a set of equations, it has an obvious advantage, i.e., it describes the flow of signals from one point of a system to another. Thus it provides cause-and-effect relationship between signals. And it often significantly reduces the work involved, and also yields an easy, systematic manipulation of variables of interest. Mason's formula is very powerful, but it is applicable only when the desired quantity is the transmission gain between the source node and sink node. In this paper, author summarizes the signal-flow graph technique, and stipulates three rules for conversion of an arbitrary nonsource node into a source node. Then heuses the conversion rules to obtain various quantities, i.e., networks gains, functions and parameters, through simple graphical manipulations.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.13-22
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• 1993

SAS/GRAPH is a part of the SAS System which generates information and presentation color graphics. It is able to import any SAS dataset from other statistical data analysis procedures and produce sophisticated graphics output. It also supports most output devices on the market and offers various tools enhancing graphics output. In this regard SAS/GRAPH outclasses its compertitors. However, it does not support an interactive tool for data visualization and graphical data analysis. As far as interactive statistical graphics is concerned, SAS/GRAPH is behind in features and functions, compared to newly emerged statsitical graphics software such as S-Plus.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.482-487
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• 2015

This paper presents the results and the process of extracting correlations between events of prescriptions and examinations using graph-modeling and node centrality measures on a medical dataset of 11,938 patients with diabetes mellitus. As the data is stored in relational form, RDB2Graph framework was used to construct effective graph models from the data. Personalized PageRank was applied to analyze correlation between prescriptions and examinations of the patients. Two graph models were constructed: one that models medical events by each patient and another that considers the time gap between medical events. The results of the correlation analysis confirm current medical knowledge. The paper demonstrates some of the note-worthy findings to show the effectiveness of the method used in the current analysis.

• Journal of IKEEE
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• v.27 no.1
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• pp.93-102
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• 2023

As one of the techniques for analyzing malicious code, techniques creating a sequence or a graph of function call relationships in an executable program and then analyzing the result are proposed. Such methods generally study function calling in the executable program code through static analysis and organize function call relationships into a sequence or a graph. However, in the case of an obfuscated executable program, it is difficult to analyze the function call relationship only with static analysis because the structure/content of the executable program file is different from the standard structure/content. In this paper, we propose a dynamic analysis method to analyze the function call relationship of an obfuscated execution program. We suggest constructing a function call relationship as a graph using the proposed technique.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.39-47
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• 2015

The centrality is calculated to describe the importance of a node in a graph and ranking is given according to the centrality for each node. There are many centrality measures and we use degree centrality, closeness centrality, eigenvector centrality, and betweenness centrality. In this paper, we analyze the effect of an additional edge of a graph on centrality and ranking through experimental computations. It is found that the effect of an additional edge on centrality and ranking of the nodes in the graph is different according to the graph structure using PCA. The results can be used for define the graph characteristics.