• Journal of applied mathematics & informatics
• /
• 제5권1호
• /
• pp.141-152
• /
• 1998

Based on the geometric representation an efficient al-gorithm is designed to find all articulation points of a permutation graph. The proposed algorithm takes only O(n log n) time and O(n) space where n represents the number of vertices. The proposed se-quential algorithm can easily be implemented in parallel which takes O(log n) time and O(n) processors on an EREW PRAM. These are the first known algorithms for the problem on this class of graph.

• Structural Engineering and Mechanics
• /
• 제14권6호
• /
• pp.625-647
• /
• 2002

Parallel execution of computational mechanics codes requires efficient mesh-partitioning techniques. These mesh-partitioning techniques divide the mesh into specified number of submeshes of approximately the same size and at the same time, minimise the interface nodes of the submeshes. This paper describes a new mesh partitioning technique, employing Genetic Algorithms. The proposed algorithm operates on the deduced graph (dual or nodal graph) of the given finite element mesh rather than directly on the mesh itself. The algorithm works by first constructing a coarse graph approximation using an automatic graph coarsening method. The coarse graph is partitioned and the results are interpolated onto the original graph to initialise an optimisation of the graph partition problem. In practice, hierarchy of (usually more than two) graphs are used to obtain the final graph partition. The proposed partitioning algorithm is applied to graphs derived from unstructured finite element meshes describing practical engineering problems and also several example graphs related to finite element meshes given in the literature. The test results indicate that the proposed GA based graph partitioning algorithm generates high quality partitions and are superior to spectral and multilevel graph partitioning algorithms.

• 방사성폐기물학회지
• /
• 제18권1호
• /
• pp.19-29
• /
• 2020

This paper gives two graph-based algorithms for radioactive decay computation. The first algorithm identifies the connected components of the graph induced from the given radioactive decay dynamics to reduce the size of the problem. The solutions are derived over the precalculated connected components, respectively and independently. The second algorithm utilizes acyclic structure of radioactive decay dynamics. The algorithm evaluates the reachable vertices of the induced system graph from the initially activated vertices and finds the minimal set of starting vertices populating the entire reachable vertices. Then, the decay calculations are performed over the reachable vertices from the identified minimal starting vertices, respectively, with the partitioned initial value over the reachable vertices. Formal arguments are given to show that the proposed graph inspired divide and conquer calculation methods perform the intended radioactive decay calculation. Empirical efforts comparing the proposed radioactive decay calculation algorithms are presented.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2017년도 춘계학술발표대회
• /
• pp.816-819
• /
• 2017

Current graph mining algorithms suffers from performance issues when querying patterns are in increasingly massive network graphs. However, from our observation most data graphs inherently contains recurring semantic subgraphs/substructures. Most graph mining algorithms treat them as independent subgraphs and perform computations on them redundantly, which result in performance degradation when processing massive graphs. In this paper, we propose an algorithm which exploits these inherent recurring subgraphs/substructures to reduce graph sizes so that redundant computations performed by the traditional graph mining algorithms are reduced. Experimental results show that our graph compression approach achieve up to 69% reduction in graph sizes over the real datasets. Moreover, required time to construct the compressed graphs is also reasonably reduced.

• Journal of applied mathematics & informatics
• /
• 제17권1_2_3호
• /
• pp.1-23
• /
• 2005

The shortest-paths problem is a fundamental problem in graph theory and finds diverse applications in various fields. This is why shortest path algorithms have been designed more thoroughly than any other algorithm in graph theory. A large number of optimization problems are mathematically equivalent to the problem of finding shortest paths in a graph. The shortest-path between a pair of vertices is defined as the path with shortest length between the pair of vertices. The shortest path from one vertex to another often gives the best way to route a message between the vertices. This paper presents an $O(n^2)$ time sequential algorithm and an $O(n^2/p+logn)$ time parallel algorithm on EREW PRAM model for solving all pairs shortest paths problem on circular-arc graphs, where p and n represent respectively the number of processors and the number of vertices of the circular-arc graph.

• 한국정보통신학회논문지
• /
• 제6권5호
• /
• pp.653-660
• /
• 2002

본 논문은 관계형 데이터베이스 기반하여 그래프를 저장하고 그래프 알고리즘을 정의할 수 있는 방법을 제안한다. 이 방법에서 그래프는 릴레이션으로 표현되며, 그래프의 각 정점과 간선은 이 릴레이션의 튜플로서 데이터베이스에 저장된다. 이를 위해 그래프의 저장 및 관리뿐만 아니라 다양한 응용프로그램 개발에도 사용될 수 있는 기본적인 그래프 함수들을 라이브러리로 개발하였다. 또한, 그래프에 대한 알고리즘을 추출, 선택, 죠인과 같은 관계대수 연산을 이용하여 정의하였으며, SQL과 같은 데이터베이스 언어를 사용하여 구현하였다. 이와 같은 데이터베이스에 기반한 방법은 메모리에 수용되지 않는 크기의 그래프를 효과적으로 처리할 수 있을 뿐만 아니라 다양한 응용프로그램 개발을 용이하게 할 것이다.

• 한국정보과학회논문지:데이타베이스
• /
• 제29권5호
• /
• pp.347-357
• /
• 2002

그래프는 실세계의 많은 문제를 효과적으로 모델링하여 해를 구할 수 있는 강력한 방법을 제공하기 때문에 그래프의 표현 방법과 알고리즘 개발에 다양한 연구가 진행되어 왔다. 하지만, 대부분의 연구가 메인 메모리에 수용 가능한 크기를 갖는 그래프만을 고려하였기 때문에 큰 문제에 적용하기 위해서는 아직도 많은 어려움이 존재한다. 이를 극복하기 위하여 본 논문에서는 관계형 데이타베이스 이론에 기반하여 그래프를 표현하고 그래프 알고리즘을 정의할 수 있는 방법을 제안한다. 이 방법에서 그래프는 릴레이션으로 표현되며 그래프의 각 정점과 간선은 이 릴레이션의 튜플로서 저장된다. 이렇게 저장된 그래프에 대한 알고리즘은 추출, 선택, 죠인과 같은 관계대수 연산을 이용하여 정의되며 SQL과 같은 데이타베이스 언어를 사용하여 구현될 수 있다. 또한, 본 논문은 그래프의 저장 및 관리뿐만 아니라 다양한 응용프로그램 개발에도 사용될 수 있는 기본적인 그래프 함수들을 라이브러리화 하였다. 이와 같은 데이터베이스에 기반한 방법은 메모리에 수용되지 않는 크기의 그래프를 효과적으로 처리할 수 있는 방법을 제공할 뿐만 아니라 다양한 응용프로그램 개발을 용이하게 할 것이다. 또한, 데이타베이스가 제공하는 기본적인 기능인 다중사용자에 의한 동시공용 등과 같은 많은 장점을 가진다.

• ETRI Journal
• /
• 제38권3호
• /
• pp.540-550
• /
• 2016

Spectral clustering is a powerful tool for exploratory data analysis. Many existing spectral clustering algorithms typically measure the similarity by using a Gaussian kernel function or an undirected k-nearest neighbor (kNN) graph, which cannot reveal the real clusters when the data are not well separated. In this paper, to improve the spectral clustering, we consider a robust similarity measure based on the shared nearest neighbors in a directed kNN graph. We propose two novel algorithms for spectral clustering: one based on the number of shared nearest neighbors, and one based on their closeness. The proposed algorithms are able to explore the underlying similarity relationships between data points, and are robust to datasets that are not well separated. Moreover, the proposed algorithms have only one parameter, k. We evaluated the proposed algorithms using synthetic and real-world datasets. The experimental results demonstrate that the proposed algorithms not only achieve a good level of performance, they also outperform the traditional spectral clustering algorithms.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
• /
• pp.687-690
• /
• 1988

Algorithms that represent given pattern in the form of an ARG (Attributed relational graph) using not only structural relations but also symbolic or numerical attributes, and then recognize that pattern by graph matching process are presented in this paper. Based on definitions of pattern deformational models, algorithms that can find GPECI(Graph preserved error correcting isomorphism). SGECI(subgraph ECI) and DSECI(Double subgraph ECI) are proposed and comparisons among these algorithms are described. To be useful in performig practical tasks, efficient schemes for extraction of ARG representation fron raw image are needed. In this study, given patterns are restricted within objects having distinct skeleton, and then the information which is necessary for recognition and analysis is successfully extracted.

• Journal of applied mathematics & informatics
• /
• 제8권2호
• /
• pp.387-401
• /
• 2001

If the distance between two vertices becomes longer after the removal of a vertex u, then u is called a hinge vertex. In this paper, a linear time sequential algorithm is presented to find all hinge vertices of an interval graph. Also, a parallel algorithm is presented which takes O(n/P + log n) time using P processors on an EREW PRAM.