• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.228-233
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• 2005

The protein side-chain packing problem (SCPP) is known to be NP-complete. Various graph theoretic based side-chain packing algorithms have been proposed. However as the size of the protein becomes larger, the sampling space increases exponentially. Hence, one approach to cope with the time complexity is to decompose the graph of the protein into smaller subgraphs. Some existing approaches decompose the graph into biconnected components at an articulation point (resulting in an at-most 21-residue subgraph) or solve the SCPP by tree decomposition (4-, 5-residue subgraph). In this regard, we had also presented a deterministic based approach called as SPWCQ using the notion of maximum edge weight clique in which we reduce SCPP to a graph and then obtain the maximum edge-weight clique of the obtained graph. This algorithm performs well for a protein of less than 500 residues. However, it fails to produce a feasible solution for larger proteins because of the size of the search space. In this paper, we present a new heuristic approach for the side-chain packing problem based on the maximum edge-weight clique finding algorithm that enables us to compute the side-chain packing of much larger proteins. Our new approach can compute side-chain packing of a protein of 874 residues with an RMSD of 1.423${\AA}$.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.1
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• pp.28-33
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• 1985

A modified directed product graph (DPGm) is proposed for the numerator of the network functions of a given non-reciprocal network. By this, the numerator can be obtained topologically and systematically without the sign rule of the Mason's formula and without the change of topological prcperties of the net work throughout the processes. And by taking the subgraph of the DPGm for each vertex, a number of cancelling terms can be removed mechanically from the DPGm beforehand and there(ore the above can be acquired more simply and rapidly.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.432-436
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• 2010

Finding communities from network data including social networks can be done by clustering the nodes of the network as densely interconnected groups, where keeping interconnection between groups sparse. To exploit a clustering algorithm for community detection task, we need a well-defined similarity measure between network nodes. In this paper, we propose a new similarity measure named "Common Neighborhood Sub-graph density" and combine the similarity with affinity propagation, which is a recently devised clustering algorithm.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.28A no.10
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• pp.847-854
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• 1991

This paper presents a new output phase assignment algorithm which determines the phases of all the nodes in a given boolean network. An estimation function is defined, which is represented by the relation between the literals in the given function expression. A weight function, WT (fi, fj) is defined, which is represented by approximate amount of common subexpression between function fi and fj. Common Subexpression Graph(CSG) is generated for phase selection by the weight function between all given functions. We propose a heuristic algorithm finding subgraph of which sum of weights has maximum by assigning phases into the given functions. The experiments with MCNC benchmarks show the efficiency of the proposed method.

• Journal of applied mathematics & informatics
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• v.12 no.1_2
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• pp.21-29
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• 2003

Let G be a graph with n vertices and n edges. The problem of constructing a spanning tree is to find a connected subgraph of G with n vertices and n -1 edges. In this paper, we propose an O(log n) time parallel algorithm with O(n/ log n) processors on an EREW PRAM for constructing a spanning tree on trapezoid graphs.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.437-450
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• 2009

In this paper, we propose seven edge labeling methods. The methods produce three case of edge labels-sets of Fibonacci numbers {$F_k|k\;{\geq}\;2$}, {$F_{2k}|k\;{\geq}\;1$} and {$F_{3k+2}|k\;{\geq}\;0$}. When a sort of interconnection network, the circulant graph is designed, these edge labels are used for its jump sequence. As a result, the degree is due to the edge labels.

• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.281-283
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• 2005

Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1129-1144
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• 2020

The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the high frequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposed based on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image is decomposed into low frequency subgraphs and high frequency subgraphs with different resolutions using wavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphs which represent structural information of the image. At the same time, for the high frequency subgraphs which represent textural information of the image, the extracted and repaired edge contour information is used to constrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texture synthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved method is compared with the existing three methods. It is found that the improved method is superior to them in inpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchical method combined with structural constraints has a good effect on the edge damage of thangka images.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.349-350
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• 2018

최근 빅데이터를 비롯한 각종 실험 장비의 발전에 따라 첨단 분야에서의 과학데이터가 급격히 증가하고 있는 가운데, 그래프 매칭은 컴퓨터 네트워크 모니터링, 소셜 네트워크의 진화 분석, 생물학 네트워크에서 모티프(motif) 탐지 등 네트워크 분석 및 데이터 마이닝 분야에서 널리 활용되고 있다. 이와 같이, 폭발적으로 증가하는 데이터에 대한 네트워크 모델링 및 유사 그래프 매칭 분석을 수행하기 위한 연구 및 기반 기술 개발은 필수적인 실정이다. 본 논문에서는 이미 확보된 대용량 그래프에서 유사한 형태의 서브 그래프를 매칭할 수 있는 GUI(Graphic User Interface)기반의 생성 도구를 제안한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.6
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• pp.417-426
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• 2013

The Korea Internet AS-level topology was constructed using three data sources: Border Gateway Protocol (BGP) trace collector, Internet Routing Registry (IRR), and Internet Exchange Point (IXP). It has 685 nodes and 1,428 links. The Korea Internet AS-level topology is a small regional subgraph of the massive global one. We investigate how well the Korea Internet preserves the topological characteristics of the global one or how different they are. We carefully select several topology metrics that can analyze the characteristics of the Korea Internet AS-level topology. We also investigate how well Internet topology generators can represent the characteristics of the Korea Internet AS-level topology.