• Journal of Applied and Pure Mathematics
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• 제5권3_4호
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• pp.237-253
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• 2023

Let a graph G = (V, E) be a (p, q) graph. Define $${\rho}=\{\array{{\frac{p}{2}} & \;\;p\text{ is even} \\ {\frac{p-1}{2}} & \;\;p\text{ is odd,}$$ and M = {±1, ±2, … ± ρ} called the set of labels. Consider a mapping λ : V → M by assigning different labels in M to the different elements of V when p is even and different labels in M to p - 1 elements of V and repeating a label for the remaining one vertex when p is odd. The labeling as defined above is said to be a pair mean cordial labeling if for each edge uv of G, there exists a labeling ${\frac{{\lambda}(u)+{\lambda}(v)}{2}}$ if λ(u) + λ(v) is even and ${\frac{{\lambda}(u)+{\lambda}(v)+1}{2}}$ if λ(u) + λ(v) is odd such that ${\mid}{\bar{{\mathbb{S}}}}_{\lambda}{_1}-{\bar{{\mathbb{S}}}}_{{\lambda}^c_1}{\mid}{\leq}1$ where ${\bar{{\mathbb{S}}}}_{\lambda}{_1}$ and ${\bar{{\mathbb{S}}}}_{{\lambda}^c_1}$ respectively denote the number of edges labeled with 1 and the number of edges not labeled with 1. A graph G for which there exists a pair mean cordial labeling is called a pair mean cordial graph. In this paper, we investigate the pair mean cordial labeling behavior of few graphs including the closed helm graph, web graph, jewel graph, sunflower graph, flower graph, tadpole graph, dumbbell graph, umbrella graph, butterfly graph, jelly fish, triangular book graph, quadrilateral book graph.

• 한국정보처리학회논문지
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• 제6권3호
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• pp.571-579
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• 1999

A Macro-Star graph which has a star graph as a basic module has node symmetry, maximum fault tolerance, and hierarchical decomposition property. And, it is an interconnection network which improves a network cost against a star graph. A matrix star graph also has such good properties of a Macro-Star graph and is an interconnection network which has a lower network cost than a Maco-Star graph. In this paper, we propose a method to embed between a Macro-Star graph and a matrix star graph. We show that a Macro-Star graph MS(k, n) can be embedded into a matrix star graph MS\ulcorner with dilation 2. In addition, we show that a matrix star graph MS\ulcorner can be embedded into a Macro-Star graph MS(k,n+1) with dilation 4 and average dilation 3 or less as well. This result means that several algorithms developed in a star graph can be simulated in a matrix star graph with constant cost.

• Annals of Fuzzy Mathematics and Informatics
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• 제16권3호
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• pp.363-371
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• 2018

The main objective of this paper is to connect fuzzy theory, graph theory and fuzzy graph theory with algebraic structure. We introduce the notion of fuzzy graph of semigroup, the notion of fuzzy ideal graph of semigroup as a generalization of fuzzy ideal of semigroup, intuitionistic fuzzy ideal of semigroup, fuzzy graph and graph, the notion of isomorphism of fuzzy graphs of semigroups and regular fuzzy graph of semigroup and we study some of their properties.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1407-1423
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• 2020

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.

• 한국지능시스템학회논문지
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• 제21권4호
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• pp.493-498
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• 2011

본 논문은 측정값의 우도를 기준으로 선택적인 측정값 적용을 통한 향상된 GraphSLAM을 제안하였다. GraphSLAM은 로봇의 이동 경로와 환경에 대한 지도를 전체 입력 데이터를 통해 추정한다. 그러나 잡음이 강한 환경에서 센서의 측정치가 부정확한 경우가 늘어나면, 전체 입력 데이터를 사용하는 GraphSLAM의 경우 정확성이 크게 떨어지게 된다. 그러므로 본 논문에서는 들어오는 센서의 측정값들을 선별하여 GraphSLAM에 적용하는 방법을 제안한다. 이 방법을 통해 잡음이 강한 환경에서 기존의 GraphSLAM보다 향상된 성능을 제공할 수 있다.

• 충청수학회지
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• 제34권3호
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• pp.285-294
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• 2021

In the present work we introduce Gaussian vertex prime labeling and investigate Gaussian vertex prime labeling for gear graph, book graph, wheel graph, kayak paddle, one point union of gear graph and one point union of wheel graph.

• 컴퓨터교육학회논문지
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• 제13권5호
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• pp.91-102
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• 2010

스타 그래프는 노드 대칭성, 최대 고장 허용도, 계층적 분할 성질을 갖고, 하이퍼큐브보다 망 비용이 개선된 널리 알려진 상호 연결망이다. 본 연구에서는 스타 그래프와 그의 변형된 그래프들 상호 간의 임베딩 방법을 제안한다. 버블정렬 그래프가 팬케익 그래프와 스타 그래프에 각각 연장율 3, 확장율 1로 임베딩 가능함을 보이고, 팬케익 그래프가 버블정렬그래프에 임베딩 하는 연장율 비용이 O($n^2$)임을 보인다. 그리고 스타 그래프가 팬케익 그래프에 연장율 4, 확장율 1로 임베딩 가능함을 보인다. 또한 스타그래프를 버블정렬 그래프에, 팬케익 그래프를 스타 그래프에 임베딩 하는 연장율 비용이 각각 O(n)임을 보인다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.800-807
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• 2022

This study aims to review the use of graph databases in construction research. Based on the diagnosis of the current research status, a future research direction is proposed. The use of graph databases in construction research has been increasing because of the efficiency in expressing complex relations between entities in construction big data. However, no study has been conducted to review systematically the status quo of graph databases. This study analyzes 42 papers in total that deployed a graph model and graph database in construction research, both quantitatively and qualitatively. A keyword analysis, topic modeling, and qualitative content analysis were conducted. The review identified the research topics, types of data sources that compose a graph, and the graph database application methods and algorithms. Although the current research is still in a nascent stage, the graph database research has great potential to develop into an advanced stage, fused with artificial intelligence (AI) in the future, based on the active usage trends this study revealed.

• Journal of applied mathematics & informatics
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• 제41권2호
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• pp.287-293
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• 2023

In this paper, the Augmented graph Es(τ) of the inverse graph Gs(τ) of a cyclic group (τ,◦) was studied. The Augmented inverse graph was constructed by applying the method of Mycielski's construction. The dimension of Augmented inverse graph and different properties of the graph were investigated. Later the chromatic number of Augmented inverse graph was discussed and the relation between the maximum degree of the graph and the chromatic number was established. In the Mycielski's construction, the properties of the key node 'u' in Es (τ) were established based on cardinality of the cyclic group (τ,◦) and also proved that the Augmented inverse graph Es(τ) was a triangle free graph.

• 대한수학회지
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• 제47권3호
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• pp.601-631
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• 2010

In [6] and [7], we introduced graph von Neumann algebras which are the (groupoid) crossed product algebras of von Neumann algebras and graph groupoids via groupoid actions. We showed that such crossed product algebras have the graph-depending amalgamated reduced free probabilistic properties. In this paper, we will consider a scalar-valued $W^*$-probability on a given graph von Neumann algebra. We show that a diagonal graph $W^*$-probability space (as a scalar-valued $W^*$-probability space) and a graph W¤-probability space (as an amalgamated $W^*$-probability space) are compatible. By this compatibility, we can find the relation between amalgamated free distributions and scalar-valued free distributions on a graph von Neumann algebra. Under this compatibility, we observe the scalar-valued freeness on a graph von Neumann algebra.