• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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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.

• Journal of the Korean Mathematical Society
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• v.52 no.1
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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 $xy{\in}S(I)$.

• Proceedings of the IEEK Conference
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• 2002.07b
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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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• v.44 no.5
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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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• v.19 no.6
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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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• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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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.

• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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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.

• Journal of Korea Multimedia Society
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• v.6 no.4
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2287-2297
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• 2007

The proposed ANIDS(Advanced Network Intrusion Detection System) which is network-based IDS using Association Rule Mining, collects the packets on the network, analyze the associations of the packets, generates the pattern graph by using the highly associated packets using Association Rule Mining, and detects the intrusion by using the generated pattern graph. ANIDS consists of PMM(Packet Management Module) collecting and managing packets, PGGM(Pattern Graph Generate Module) generating pattern graphs, and IDM(Intrusion Detection Module) detecting intrusions. Specially, PGGM finds the candidate packets of Association Rule large than $Sup_{min}$ using Apriori algorithm, measures the Confidence of Association Rule, and generates pattern graph of association rules large than $Conf_{min}$. ANIDS reduces the false positive by using pattern graph even before finalizing the new pattern graph, the pattern graph which is being generated is compared with the existing one stored in DB. If they are the same, we can estimate it is an intrusion. Therefore, this paper can reduce the speed of intrusion detection and the false positive and increase the detection ratio of intrusion.