• Title/Summary/Keyword: Linear Graph

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An Algorithm for Drawing Metabolic Pathways based on Structural Characteristics (구조적 특징에 기반한 대사 경로 드로잉 알고리즘)

  • 이소희;송은하;이상호;박현석
    • Journal of KIISE:Software and Applications
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    • v.31 no.10
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    • pp.1266-1275
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    • 2004
  • Bioinformatics is concerned with the creation and development of advanced information and computational technologies for problems in biology. It is divided into genomics, proteomics and metabolimics. In metabolimics, an organism is represented by metabolic pathway, i.e., well-displayed graph, and so the graph drawing tool to draw pathway well is necessary to understand it comprehensively. In this paper, we design an improved drawing algorithm. It enhances the readability by making use of the bipartite graph. Also it is possible to draw large graph properly by considering the facts that metabolic pathway graph is scale-free network and is composed of circular components, hierarchic components and linear components.

An efficient seismic analysis of regular skeletal structures via graph product rules and canonical forms

  • Kaveh, A.;Zakian, P.
    • Earthquakes and Structures
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    • v.10 no.1
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    • pp.25-51
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    • 2016
  • In this study, graph product rules are applied to the dynamic analysis of regular skeletal structures. Graph product rules have recently been utilized in structural mechanics as a powerful tool for eigensolution of symmetric and regular skeletal structures. A structure is called regular if its model is a graph product. In the first part of this paper, the formulation of time history dynamic analysis of regular structures under seismic excitation is derived using graph product rules. This formulation can generally be utilized for efficient linear elastic dynamic analysis using vibration modes. The second part comprises of random vibration analysis of regular skeletal structures via canonical forms and closed-form eigensolution of matrices containing special patterns for symmetric structures. In this part, the formulations are developed for dynamic analysis of structures subjected to random seismic excitation in frequency domain. In all the proposed methods, eigensolution of the problems is achieved with less computational effort due to incorporating graph product rules and canonical forms for symmetric and cyclically symmetric structures.

Biplots of Multivariate Data Guided by Linear and/or Logistic Regression

  • Huh, Myung-Hoe;Lee, Yonggoo
    • Communications for Statistical Applications and Methods
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    • v.20 no.2
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    • pp.129-136
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    • 2013
  • Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.

Algorithm for Maximum Cycle Detection of Directed and Undirected General Graphs (방향과 무 방향 일반 그래프의 최대 사이클 검출 알고리즘)

  • Lee, Sang-Un
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.22 no.6
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    • pp.91-97
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    • 2022
  • There is hare and tortoise racing algorithm(HTA) for single-source(SS) singly linked list(SLL) with O(n) time complexity. But the fast method is unknown for general graph with multi-source, multi-destination, and multi-branch(MSMDMB). This paper suggests linear time cycle detection algorithm for given undirected and digraph with MSMDMB. The proposed method reduced the given graph G contained with unnecessary vertices(or nodes) to cycle into reduced graph G' with only necessary vertices(or nodes) to cycle based on the condition of cycle formation. For the reduced graph G', we can be find the cycle set C and cycle length λ using linear search within linear time. As a result of experiment data, the proposed algorithm can be obtained the cycle for whole data.

LINEAR EXTENSIONS OF DIAMOND POSETS

  • Ju, Hyeong-Kwan;Seo, Seunghyun
    • Honam Mathematical Journal
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    • v.41 no.4
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    • pp.863-870
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    • 2019
  • In this paper, we obtain the enumeration results on the number of linear extensions of diamond posets. We find the recurrence relations and exponential generating functions for the number of linear extensions of diamond posets. We also get some results for the volume of graph polytope associated with bipartite graphs which are underlying graphs of diamond posets.

Combining Local and Global Features to Reduce 2-Hop Label Size of Directed Acyclic Graphs

  • Ahn, Jinhyun;Im, Dong-Hyuk
    • Journal of Information Processing Systems
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    • v.16 no.1
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    • pp.201-209
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    • 2020
  • The graph data structure is popular because it can intuitively represent real-world knowledge. Graph databases have attracted attention in academia and industry because they can be used to maintain graph data and allow users to mine knowledge. Mining reachability relationships between two nodes in a graph, termed reachability query processing, is an important functionality of graph databases. Online traversals, such as the breadth-first and depth-first search, are inefficient in processing reachability queries when dealing with large-scale graphs. Labeling schemes have been proposed to overcome these disadvantages. The state-of-the-art is the 2-hop labeling scheme: each node has in and out labels containing reachable node IDs as integers. Unfortunately, existing 2-hop labeling schemes generate huge 2-hop label sizes because they only consider local features, such as degrees. In this paper, we propose a more efficient 2-hop label size reduction approach. We consider the topological sort index, which is a global feature. A linear combination is suggested for utilizing both local and global features. We conduct experiments over real-world and synthetic directed acyclic graph datasets and show that the proposed approach generates smaller labels than existing approaches.

Effective Web Crawling Orderings from Graph Search Techniques (그래프 탐색 기법을 이용한 효율적인 웹 크롤링 방법들)

  • Kim, Jin-Il;Kwon, Yoo-Jin;Kim, Jin-Wook;Kim, Sung-Ryul;Park, Kun-Soo
    • Journal of KIISE:Computer Systems and Theory
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    • v.37 no.1
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    • pp.27-34
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    • 2010
  • Web crawlers are fundamental programs which iteratively download web pages by following links of web pages starting from a small set of initial URLs. Previously several web crawling orderings have been proposed to crawl popular web pages in preference to other pages, but some graph search techniques whose characteristics and efficient implementations had been studied in graph theory community have not been applied yet for web crawling orderings. In this paper we consider various graph search techniques including lexicographic breadth-first search, lexicographic depth-first search and maximum cardinality search as well as well-known breadth-first search and depth-first search, and then choose effective web crawling orderings which have linear time complexity and crawl popular pages early. Especially, for maximum cardinality search and lexicographic breadth-first search whose implementations are non-trivial, we propose linear-time web crawling orderings by applying the partition refinement method. Experimental results show that maximum cardinality search has desirable properties in both time complexity and the quality of crawled pages.

A Construction of the Linear Digital Switching Function over Finite Fields (유한체상에서의 선형디지털스위칭함수 구성)

  • Park, Chun-Myoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.12 no.12
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    • pp.2201-2206
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    • 2008
  • This paper presents a method of constructing the Linear Digital Switching Function(LDSF) over finite fields. The proposed method is as following. First of all, we extract the input/output relationship of linear characteristics for the given digital switching functions, Next, we convert the input/output relationship to Directed Cyclic Graph(DCG) using basic gates adder and coefficient multiplier that are defined by mathematical properties in finite fields. Also, we propose the new factorization method for matrix characteristics equation that represent the relationship of the input/output characteristics. The proposed method have properties of generalization and regularity. Also, the proposed method is possible to any prime number multiplication expression.

A Stability Analysis of the Magnetic Bearing System Subject to Sensor dislocation Error -Discussion on Nonlinear Magnetic Force Model- (센서의 설치 오차에 따른 자기베어링 지지 로터계의 안정도에 관한 연구 -비선형 자기력 모델에 대한 고찰-)

  • 정재일;김종원
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1996.11a
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    • pp.799-805
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    • 1996
  • In many cases, the magnetic farce model is linearized at the origin in designing the controller of a magnetic bearing system. However. this linear assumption is violated by the unmodeled nonlinear effect such as sensor dislocation and backup bearing dislocation. Therefore, a direct probe into the nonlinear magnetic force model in an active magnetic bearing system is necessary. To analyze the nonlinear magnetic force model of a magnetic bearing system, phase plot analysis which is to plot the numerical solution of the nonlinear equation in several initial points in the interested region is applied. Phase plot analysis is used to observe a nonlinear dynamic system qualitatively (not quantitatively). With this method, we can get much useful information of the nonlinear system. Among this information, a bifurcation graph that represents stability and locations of fixed points is essential. From the bifurcation graph, a stability criterion of magnetic bearing system is derived.

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Research on Performance of Graph Algorithm using Deep Learning Technology (딥러닝 기술을 적용한 그래프 알고리즘 성능 연구)

  • Giseop Noh
    • The Journal of the Convergence on Culture Technology
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    • v.10 no.1
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    • pp.471-476
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    • 2024
  • With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.