• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2476-2495
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• 2021

Model-based software architecture verification and test scenarios generation are becoming more and more important in the software industry. Based on the existing temporal graph grammar, this paper proposes a new formalization method of the context-sensitive graph grammar for aiming at UML activity diagrams, which is called the UML Activity Graph Grammar, or UAGG. In the UAGG, there are new definitions and parsing algorithms. The proposed mechanisms are able to not only check the structural correctness of the UML activity diagram but also automatically generate the test scenario according to user constraints. Finally, a case study is discussed to illustrate how the UAGG and its algorithms work.

• 대한수학회지
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• 제52권1호
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• pp.159-176
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• 2015

Let R be a semiring, I a strong co-ideal of R and S(I) the set of all elements of R which are not prime to I. In this paper we investigate some interesting properties of S(I) and introduce the total identity-summand graph of a semiring R with respect to a co-ideal I. It is the graph with all elements of R as vertices and for distinct x, $y{\in}R$, the vertices x and y are adjacent if and only if $xy{\in}S(I)$.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.959-962
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• 2002

This paper presents a method of constructing the universal multiple processing element unit(UMPEU) based on De Bruijn Graph. The proposed method is as following. Firstly we propose transformation operators in order to construct the De Bruijn graph using properties of graph. Secondly we construct the transformation table of De Bruijn graph using above transformation operators. Finally we construct the De Bruijn graph using transformation table. The proposed UMPEU is capable of constructing the De Bruijn geraph for any prime number and integer value of finite fields. Also the UMPEU is applied to fault-tolerant computing system, pipeline class, parallel processing network, switching function and its circuits.

• ETRI Journal
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• 제44권5호
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• pp.780-793
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• 2022

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

• Journal of Information Processing Systems
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• 제19권6호
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• pp.803-816
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• 2023

Explainable artificial intelligence is a method that explains how a complex model (e.g., a deep neural network) yields its output from a given input. Recently, graph-type data have been widely used in various fields, and diverse graph neural networks (GNNs) have been developed for graph-type data. However, methods to explain the behavior of GNNs have not been studied much, and only a limited understanding of GNNs is currently available. Therefore, in this paper, we propose an explanation method for node classification using graph convolutional networks (GCNs), which is a representative type of GNN. The proposed method finds out which features of each node have the greatest influence on the classification of that node using GCN. The proposed method identifies influential features by backtracking the layers of the GCN from the output layer to the input layer using the gradients. The experimental results on both synthetic and real datasets demonstrate that the proposed explanation method accurately identifies the features of each node that have the greatest influence on its classification.

• International Journal of Computer Science & Network Security
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• 제24권5호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.147-169
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• 2024

In pace with the development of network technology at lightning speed, social networks have been extensively applied in our lives. However, as social networks retain a large number of users' sensitive information, the openness of this information makes social networks vulnerable to attacks by malicious attackers. To preserve the link privacy of individuals in social networks, an uncertain graph method based on node random response is devised, which satisfies differential privacy while maintaining expected data utility. In this method, to achieve privacy preserving, the random response is applied on nodes to achieve edge modification on an original graph and node differential privacy is introduced to inject uncertainty on the edges. Simultaneously, to keep data utility, a divide and conquer strategy is adopted to decompose the original graph into many sub-graphs and each sub-graph is dealt with separately. In particular, only some larger sub-graphs selected by the exponent mechanism are modified, which further reduces the perturbation to the original graph. The presented method is proven to satisfy differential privacy. The performances of experiments demonstrate that this uncertain graph method can effectively provide a strict privacy guarantee and maintain data utility.

• 한국컴퓨터정보학회논문지
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• 제24권7호
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• pp.61-69
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• 2019

In this paper, a classification method of random graph models is proposed and it is based on centralities of the random graphs. Similarity between two random graphs is measured for the classification of random graph models. The similarity between two random graph models $G^{R_1}$ and $G^{R_2}$ is defined by the distance of $G^{R_1}$ and $G^{R_2}$, where $G^{R_2}$ is a set of random graph $G^{R_2}=\{G_1^{R_2},...,G_p^{R_2}\}$ that have the same number of nodes and edges as random graph $G^{R_1}$. The distance($G^{R_1},G^{R_2}$) is obtained by comparing centralities of $G^{R_1}$ and $G^{R_2}$. Through the computational experiments, we show that it is possible to compare random graph models regardless of the number of vertices or edges of the random graphs. Also, it is possible to identify and classify the properties of the random graph models by measuring and comparing similarities between random graph models.

• 한국멀티미디어학회논문지
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• 제6권4호
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• pp.736-745
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• 2003

Traditional slicing techniques make slices through dependence graphs. They also improve the accuracy of slices. However, traditional slicing techniques require many vertices and edges in order to express a data communication link because they are based on static slicing techniques. Therefore the graph becomes very complicated, and size of the slices is larger. We propose the representation of a dynamic object-oriented program dependence graph so as to process the slicing of object-oriented programs that is composed of related programs in order to process certain jobs. We also propose an efficient slicing algorithm using the relations of relative tables in order to compute dynamic slices of object-oriented programs. Consequently, the efficiency of the proposed efficient dynamic object-oriented program dependence graph technique is also compared with the dependence graph techniques discussed previously As a result, this is certifying that an efficient dynamic object-oriented program dependence graph is more efficient in comparison with the traditional object-oriented dependence graphs and dynamic object-oriented program dependence graph.

• 한국정보통신학회논문지
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• 제11권12호
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• pp.2287-2297
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• 2007

제안한 ANIDS(Advanced Network based IDS)는 네트워크 패킷을 수집하여 연관규칙 마이닝 기법을 이용하여 패킷의 연관성을 분석하고, 연관성이 높은 패킷을 이용해 패턴 그래프를 생성한 후, 생성된 패턴 그래프를 이용해 침입인지를 판단하는 네트워크 기반 침입 탐지 시스템이다. ANIDS는 패킷 수집 및 관리하는 PMM(Packet Management Module), 연관성 있는 패킷들만을 이용해 패턴 그래프를 생성하는 PGGM (Pattern Graph Generate Module), 침입을 탐지하는 IDM(Intrusion Detection Module)으로 구성된다. 특히, PGGM은 Apriori 알고리즘을 이용해 $Sup_{min}$보다 큰 연관규칙의 후보 패킷을 찾은 후, 연관규칙의 신뢰도를 측정하여 최소 신뢰도 $Conf_{min}$보다 큰 연관규칙의 패턴 그래프를 생성한다. ANIDS는 패킷간의 연관성을 분석하여 침입인지를 탐지 할 수 있는 패턴 그래프를 사용함으로써, 침입 탐지의 긍정적 결함 오류를 감소시킬 수 있으며, 완벽한 패턴 그래프 패턴이 생성되기 전에, 이미 침입으로 판정된 패턴 그래프 패턴과 비교하여 유사한 패턴 형태를 침입으로 간주하므로 기존의 침입 탐지 시스템에 비해 침입 탐지속도를 감소시키고 침입 탐지율을 증가시킬 수 있다.