• Journal of Educational Research in Mathematics
• /
• v.18 no.3
• /
• pp.353-372
• /
• 2008

This study looks around the goals of teaching statistical graphs that are introduced in the seventh Korean Curriculum for Elementary School and in the Principles and Standards for School Mathematics(NCTM, 2000), and these are compared. We compare how to transpose statistical graphs didactically between the Korean and MiC textbooks. For it, it examines the types of statistical graphs, the methods defining them, and the making connections and comparing among them, which are content components in the chapters on statistical graphs. The results show that in contrast to the Korean textbooks, NCTM(2000) has allowed students to develop their own expression for data, to compare results analysed within different graphs, and to consider a graph as a whole in the goals of teaching statistical graphs. MiC textbooks have introduced the number-line plot and the box plot more than Korean. Although both of Korean and MiC textbooks usually use extensive methods for defining individual graphs, the former use extensive methods together with synonymic methods and the latter use extensive methods with the characteristics of graphs. Also, the number-line plot is defined using operative method in the MiC textbooks. MiC textbooks contain various activities for connecting and comparing graphs, but there are comparatively few comparing activities in the Korean textbooks.

• Kyungpook Mathematical Journal
• /
• v.57 no.2
• /
• pp.301-307
• /
• 2017

In this paper, we solve the following four graph equations $L^k(G)=H{\oplus}J$; $M(G)=H{\oplus}J$; ${\bar{L^k(G)}}=H{\oplus}J$ and ${\bar{M(G)}}=H{\oplus}J$, where J is $nK_2$ for $n{\geq}1$. Here, the equality symbol = means the isomorphism between the corresponding graphs. In particular, we shall obtain all pairs of graphs (G, H), which satisfy the above mentioned equations, upto isomorphism.

• Communications for Statistical Applications and Methods
• /
• v.11 no.1
• /
• pp.21-30
• /
• 2004

Often the accuracy is not adequate as a performance measure of classifiers when costs are different for different prediction errors. ROC and cost graphs can be used in such case to compare and identify cost-sensitive classifiers. We extend ROC and cost graphs so that they can be used when more general cost matrix is given, where not only misclassifications but correct classifications also incur penalties.

• Korean Journal of Mathematics
• /
• v.31 no.4
• /
• pp.505-519
• /
• 2023

Let G = (V, E) be a graph. A subset S of V is called a dominating set of G if every vertex not in S is adjacent to some vertex in S. The domination number γ(G) of G is the minimum cardinality taken over all dominating sets of G. A dominating set S is called a connected dominating set if the subgraph induced by S is connected. The minimum cardinality taken over all connected dominating sets of G is called the connected domination number of G, and is denoted by γ_c(G). In this paper, we investigate the Nordhaus-Gaddum type results for the connected domination number and its derived graphs like line graph, subdivision graph, power graph, block graph and total graph, and characterize the extremal graphs.

• Journal of the Chungcheong Mathematical Society
• /
• v.23 no.2
• /
• pp.251-256
• /
• 2010

In this article, we consider a minimal graph in $R^3$ which is bounded by a Jordan curve and a straight line. Suppose that the boundary is symmetric with the reflection under a plane, then we will prove that the minimal graph is itself symmetric under the reflection through the same plane.

• Communications of the Korean Mathematical Society
• /
• v.35 no.2
• /
• pp.359-370
• /
• 2020

A graph G is called a well covered graph if every maximal independent set in G is maximum, and co-well covered graph if its complement is a well covered graph. We study some properties of a co-well covered graph and we characterize when the join, the corona product, and cartesian product are co-well covered graphs. Also we characterize when powers of trees and cycles are co-well covered graphs. The line graph of a graph which is co-well covered is also studied.

• School Mathematics
• /
• v.1 no.2
• /
• pp.555-569
• /
• 1999

The graph unit(chapter) is a good example of a topic in elementary school mathematics for which computer use can be incorporated as part of the instruction. Teaching graph can be facilitated by using the graphing utilities of computers, which make it possible to observe the property of many types of graphs. This study was concerned with utilizing an educational software Graphers as an instructional tool in teaching to help young students gain a better understanding of graph concepts. For this purpose, three types of instructional activities using Graphers were shown in the paper. Graphers is a data-gathering tool for creating pictorial data chosen from several data sets. They can represent their data on a table or with six types of graphs such as Pictograph, Bar Graph, Line Graph, Circle Graph, Grid Plot and Loops. They help students to select the graph(s) which are the most appropriate for the purpose of analyzing data while comparing various types of graphs. They also let them modify or change graphs, such as adding grid lines, changing the axis scale, or adding title and labels. Eventually, students have a chance to interpret graphs meaningfully and in their own way.

• Bulletin of the Korean Mathematical Society
• /
• v.59 no.1
• /
• pp.119-139
• /
• 2022

This paper deals with the λ-labeling and L(2, 1)-coloring of simple graphs. A λ-labeling of a graph G is any labeling of the vertices of G with different labels such that any two adjacent vertices receive labels which differ at least two. Also an L(2, 1)-coloring of G is any labeling of the vertices of G such that any two adjacent vertices receive labels which differ at least two and any two vertices with distance two receive distinct labels. Assume that a partial λ-labeling f is given in a graph G. A general question is whether f can be extended to a λ-labeling of G. We show that the extension is feasible if and only if a Hamiltonian path consistent with some distance constraints exists in the complement of G. Then we consider line graph of bipartite multigraphs and determine the minimum number of labels in L(2, 1)-coloring and λ-labeling of these graphs. In fact we obtain easily computable formulas for the path covering number and the maximum path of the complement of these graphs. We obtain a polynomial time algorithm which generates all Hamiltonian paths in the related graphs. A special case is the Cartesian product graph K_n☐K_n and the generation of λ-squares.

• School Mathematics
• /
• v.9 no.1
• /
• pp.45-64
• /
• 2007

The primary purposes of this study were to investigate how sixth graders would react to the types of tasks with regard to the comprehension of graphs and what differences might be among the kinds of graphs, and to raise issues about instructional methods of graphs. A descriptive study through pencil-and-paper tests was conducted. The tests consisted of 48 questions with 4 types of tasks (reading the data, reading between the data, reading beyond the data, and understanding the situations) and 6 kinds of graphs. The conclusions drawn from the results obtained in this study were as follows: First, it is necessary to foster the ability of interpreting the data and understanding the situation in graphs as well as that of reading the data and finding out the relationships in the data. Second, it is informative for teachers to know students' difficulties and thinking processes. Third, in order to develop understanding of graphs, it is important that students solve different types of tasks beyond simple question-answer tasks. Fourth, teachers need to pay attention to teach fundamental factors such as reading the data with regard to line graphs and stem-and-leaf plots Finally, graph type and task type interact to determine graph-comprehension performance. Therefore, both learning all kinds of graphs and being familiar with multiple types of tasks are important.

• Bulletin of the Korean Mathematical Society
• /
• v.37 no.1
• /
• pp.121-125
• /
• 2000

In this paper, we obtain an algorithm for finding a maximum matching in the line graph L(G) of a graph G. The complexity of our algorithm is O($$\mid$E$\mid$$), where is the edge set of G($$\mid$E$\mid$$ is equal to the number of vertices in L(G)).