• 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.

• 한국정보통신학회논문지
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• 제17권4호
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• pp.863-868
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• 2013

본 논문에서는 최근의 디지털논리시스템의 함수구성시에 도입되고 있는 그래프이론에 바탕을 둔 새로운 형태의 데이터구조 형태인 모서리값 확장 그래프를 추출하는 알고리즘을 제안하였다. 이를 위해 수학적 배경으로는 리터럴 함수와 리드 뮬러 확장에 대해 논의하였으며, 본 논문의 근간인 모서리 확장 그래프의 도출에 대해 논의하였다. 또한, 모서리 확장 그래프로부터 임의의 m치 n변수의 축약된 함수구성을 도출하는 알고리즘을 제안하였으며 이를 예에 적용하여 그 타당성을 보였다. 제안된 알고리즘의 규칙성을 고려하여 동일부분을 모듈화함으로써 일반성을 가짐을 보였다.

• 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.

• 한국정보통신학회논문지
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• 제7권5호
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• pp.993-1001
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• 2003

본 논문에서는 Edge-Labeled Graph에 기반하여 XML 인스턴스들을 관계형 데이터베이스로 저장하는 모델을 제안하고 구현한다. 저장되는 XML 인스턴스들은 Edge-Labeled Graph에 기반 한 Data Graph로 표현하고 이를 이용하여 데이터 경로, 엘리먼트, 속성, 테이블 인덱스 테이블에 정의한 값들을 추출한 후 Mapper를 이용하여 데이터베이스 스키마를 정의하고 추출된 값들을 저장한다. 그리고, 저장 모델은 질의를 지원하기 위해, XPATH를 따르는 질의 언어로 사용되는 XQL을 SQL로 변환하는 변환기 및 저장된 XML 인스턴스를 복원하는 DBtoXML 처리기를 갖도록 한다. 구현 결과, XML 인스턴스들과 제안된 모델 구조간의 저장 관계가 그래프 기반의 경로를 이용한 표현으로 가능했으며, 동시에, 특정 엘리먼트 또는 속성들의 정보들을 쉽게 검색할 수 있는 가능성을 보였다.

• 한국컴퓨터정보학회논문지
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• 제22권3호
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• pp.61-67
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• 2017

This paper analyzes the robustness of the network based on the centrality of vertices in the graph. In this paper, a random graph is generated and a modified graph is constructed by adding or removing vertices or edges in the generated random graph. And then we analyze the robustness of the graph by observing changes in the centrality of the random graph and the modified graph. In the process modifying a graph, we changes some parts of the graph, which has high values of centralities, not in the whole. We study how these additional changes affect the robustness of the graph when changes occurring a group that has higher centralities than in the whole.

• Kyungpook Mathematical Journal
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• 제62권3호
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• pp.595-613
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• 2022

Let R be a commutative ring with identity and let I be a proper ideal of R. In this paper, we study the ideal based extended zero-divisor graph 𝚪'_I (R) and prove that 𝚪'_I (R) is connected with diameter at most two and if 𝚪'_I (R) contains a cycle, then girth is at most four girth at most four. Furthermore, we study affinity the connection between the ideal based extended zero-divisor graph 𝚪'_I (R) and the ideal-based zero-divisor graph 𝚪_I (R) associated with the ideal I of R. Among the other things, for a radical ideal of a ring R, we show that the ideal-based extended zero-divisor graph 𝚪'_I (R) is identical to the ideal-based zero-divisor graph 𝚪_I (R) if and only if R has exactly two minimal prime-ideals which contain I.

• 인터넷정보학회논문지
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• 제4권6호
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• pp.33-42
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• 2003

본 논문에서는 Edge-Labeled Graph에 기반하여 XML 인스턴스들을 관계형 데이터베이스내에 저장하는 모델을 제안하고 구현한다. 저장 모델은 저장되는 XMI 인스턴스들을 Edge-Labeled Graph에 기반하여 데이터 그래프로 표현하며, 표현한 데이터 그래프상의 정보를 저장하기 위해 데이터베이스 스키마로 제시된 데이터 경로, 요소, 속성, 테이블 인덱스 테이블의 구조에 따라 정의된 값들을 추출하고 Mapper 모듈을 이용하여 저장하며 질의를 지원하기 위해, XPATH를 따르는 질의 언어인 XQL을 SQL로 변환하는 모듈, 또한 저장된 XML 인스턴스를 복원하는 DBtoXML 모듈을 갖도록 하였다. 구현 결과, XML 인스턴스들과 제안한 저장 모델 구조로의 저장 관계가 그래프 기반의 경로를 이용한 표현으로 가능했으며, 동시에, 특정 요소 또는 속성들의 정보들을 쉽게 검색할 수 있는 가능성을 보였다.

• Journal of Computing Science and Engineering
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• 제5권3호
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• pp.197-209
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• 2011

The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attacks as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. In addition, we also investigate the impact of the proposed methods on community detection, a very popular application in the graph mining field. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.

• Journal of Information Technology Applications and Management
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• 제26권1호
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.