• Journal of applied mathematics & informatics
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• 제24권1_2호
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• pp.141-154
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• 2007

The connectivity problem is a fundamental problem in graph theory. The best known algorithm to solve the connectivity problem on general graphs with n vertices and m edges takes $O(K(G)mn^{1.5})$ time, where K(G) is the vertex connectivity of G. In this paper, an efficient algorithm is designed to solve vertex connectivity problem, which takes $O(n^2)$ time and O(n) space for a trapezoid graph.

• Journal of applied mathematics & informatics
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• 제39권3_4호
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• pp.339-358
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• 2021

Zagreb indices and their modified versions of a molecular graph are important descriptors which can be used to characterize the structural properties of organic molecules from different aspects. In this work, we investigate some properties of the modified second multiplicative Zagreb index of graphs with given connectivity. In particular, we obtain the maximum values of the modified second multiplicative Zagreb index with fixed number of cut edges, or cut vertices, or edge connectivity, or vertex connectivity of graphs. Furthermore, we characterize the corresponding extremal graphs.

• 한국정보통신학회논문지
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• 제20권2호
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• pp.415-420
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• 2016

선분 그래프(interval graph) G=(V,E)는 직선 상의 선분들을 나타내는 정점 집합 V와 간선 $(i,j){\in}E$는 선분 i와 j가 교차함을 나타내는 간선들의 집합 E로 이루어진다. 본 논문에서는 그래프의 여러 특성 중에서 정점 연결성(vertex connectivity)에 주목한다. 특별히 선분들이 겹쳐지는 모습으로 선분 그래프의 정점 연결성을 나타낸다. 또한 선분 그래프에서 정점이나 간선이 추가 되거나 삭제되는 완전 동적 (fully dynamic) 환경에서 정점 연결성을 계산하는 효율적인 알고리즘을 제안할 것이다. 특별한 형태의 선분 트리(interval tree)를 사용하여 새로운 선분이 추가되거나 삭제되는 상황 하에서 정점 연결성을 계산하고 트리를 유지하는데 O(logn) 시간이 소요됨을 보일 것이다.

• Kyungpook Mathematical Journal
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• 제61권1호
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• pp.61-74
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• 2021

Let G = (V, E) be a connected graph. The eccentric connectivity index of G is defined by ��C (G) = ∑u∈V (G) deg(u)e(u), where deg(u) and e(u) denote the degree and eccentricity of the vertex u in G, respectively. In this paper, we introduce a new formulation of ��C that will be called the distance eccentric connectivity index of G and defined by $${\xi}^{De}(G)\;=\;{\sum\limits_{u{\in}V(G)}}\;deg^{De}(u)e(u)$$ where degDe(u) denotes the distance eccentricity degree of the vertex u in G. The aim of this paper is to introduce and study this new topological index. The values of the eccentric connectivity index is calculated for some fundamental graph classes and also for some graph operations. Some inequalities giving upper and lower bounds for this index are obtained.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.462-465
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• 2000

It is important to compress three dimensional (3D) data efficiently, since 3D data are too large to store or transmit in general. In this paper, we propose a lossless compression algorithm of the 3D mesh connectivity, based on the vertex degree. Most techniques for the 3D mesh compression treat the connectivity and the geometric separately, but our approach attempts to exploit the geometric information for compressing the connectivity information. We use the geometric angle constraint of the vertex fanout pattern to predict the vertex degree, so the proposed algorithm yields higher compression efficiency than the conventional algorithms.

• 대한수학회보
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• 제58권1호
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• pp.31-46
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• 2021

Let a and b be positive integers, and let V (G), ��(G), and ��2(G) be the vertex set of a graph G, the minimum degree of G, and the minimum degree sum of two non-adjacent vertices in V (G), respectively. An even [a, b]-factor of a graph G is a spanning subgraph H such that for every vertex v ∈ V (G), dH(v) is even and a ≤ dH(v) ≤ b, where dH(v) is the degree of v in H. Matsuda conjectured that if G is an n-vertex 2-edge-connected graph such that $n{\geq}2a+b+{\frac{a^2-3a}{b}}-2$, ��(G) ≥ a, and ${\sigma}_2(G){\geq}{\frac{2an}{a+b}}$, then G has an even [a, b]-factor. In this paper, we provide counterexamples, which are highly connected. Furthermore, we give sharp sufficient conditions for a graph to have an even [a, b]-factor. For even an, we conjecture a lower bound for the largest eigenvalue in an n-vertex graph to have an [a, b]-factor.

• 한국정보과학회논문지:시스템및이론
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• 제29권9호
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• pp.493-502
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• 2002

다중 컴퓨터 네트워크의 고장 감내에 대한 대표적인 결정적 척도로 연결도와 에지 연결도가 있다. 연결도나 에지 연결도는 어떤 정점 분리 집합이나 에지 분리 집합을 제거했을 때 남은 그래프의 형태를 고려하지 않는다는 문제가 있다. 이러한 단점을 보완하기 위해서 superconnectivity, toughness, scattering number, vertex-integrity, binding number, restricted connectivity와 같은 일반화된 연결성 척도들이 함께 사용된다. 이 논문에서는 재귀원형군과 하이퍼큐브의 고장 감내에 대한 이러한 결정적 척도를 분석하고, 고장 감내 측면에서 비교한다.

• Journal of applied mathematics & informatics
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• 제38권1_2호
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• pp.189-200
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• 2020

Let G be a simple connected graph with n vertices and m edges. Denote by d₁ ≥ d₂ ≥ ⋯ ≥ d_n > 0 and d(e₁) ≥ d(e₂) ≥ ⋯ ≥ d(e_m) sequences of vertex and edge degrees, respectively. If vertices v_i and v_j are adjacent, we write i ~ j. The general sum-connectivity index is defined as 𝒳_α(G) = ∑_i~j(d_i + d_j)^α, where α is an arbitrary real number. Firstly, we determine a relation between 𝒳_α(G) and 𝒳_α-1(G). Then we use it to obtain some new bounds for 𝒳_α(G).

• 대한수학회보
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• 제53권4호
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• pp.1197-1211
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• 2016

Let R be a finite commutative ring with nonzero identity. We define ${\Gamma}(R)$ to be the graph with vertex set R in which two distinct vertices x and y are adjacent if and only if there exists a unit element u of R such that x + uy is a unit of R. This graph provides a refinement of the unit and unitary Cayley graphs. In this paper, basic properties of ${\Gamma}(R)$ are obtained and the vertex connectivity and the edge connectivity of ${\Gamma}(R)$ are given. Finally, by a constructive way, we determine when the graph ${\Gamma}(R)$ is Hamiltonian. As a consequence, we show that ${\Gamma}(R)$ has a perfect matching if and only if ${\mid}R{\mid}$ is an even number.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.745-746
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• 2006

In this paper, we propose a new predictive coding scheme for color data of three-dimensional (3-D) mesh models. We exploit connectivity and geometry information to improve coding efficiency. After ordering all vertices in a 3-D mesh model with a vertex traversal technique, we employ a geometry predictor to compress the color data. The predicted color can be acquired by a weighted sum of reconstructed colors for adjacent vertices using both angles and distances between the current vertex and adjacent vertices.