• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.537-539
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• 2019

구문 분석은 문장의 구조를 분석하는 자연어처리 분야로 그래프 기반 방법과 전이 기반 방법으로 나뉘어 연구되어 왔다. 본 논문에서는 그래프 기반 방식에서 높은 성능을 보이고 있는 Deep Biaffine 어텐션 모델에 별도의 High-Order 정보 추출 없이 Graph Neural Network(GNNs)을 이용하여 High-Order 정보를 학습할 수 있도록 확장한 Deep Biaffine 어텐션 GNNs을 적용하여 한국어 세종 구문 분석 셋에서 UAS : 94.44%, LAS : 92.55%의 성능을 달성하였으며 Dual Decomposition을 통해 전이 기반 한국어 구문 분석 모델과 결합하여 추가적인 성능 향상을 보였다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.574-576
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• 2022

최근 여러 분야에서 그래프 신경망(graph neural network, GNN)이 활발히 연구되고 있다. 하지만 지금까지 대부분의 GNN 연구는 단일 GNN 모델의 성능을 향상하는 데 집중되었다. 본 논문에서는 앙상블(ensemble) 기법의 대표적 기법인 그래디언트 부스팅(gradient boosting)을 이용하여 GNN의 앙상블 모델을 만드는 방법을 제안한다. 제안 방법은 앞서 만들어진 GNN의 오차를 경사 하강법(gradient descent)을 이용하여 감소시키는 방향으로 다음 GNN을 생성한다. 이 과정을 반복하여 GNN의 최종 앙상블 모델을 얻는다. 실험에서 GNN의 대표적인 모델인 그래프 합성곱 신경망(graph convolutional network, GCN)에 제안 방법을 적용하여 앙상블 모델을 생성한 결과, 단일 GCN 모델에 비해 노드 분류 정확도가 11.3%p까지 증가하였음을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2187-2192
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• 2006

This paper presents a method of constructing the universal multiple processing element unit(UMPEU) by De Bruijn Graph. The second method is as following. First, we propose transformation operators in order to construct the De Bruijn UMPEU using properties of graph. Second, we construct the transformation table of De Bruijn graph using above transformation operators. Finally we construct the De Bruijn graph using transformation table. The proposed UMPEU be able to construct the De Bruijn graph for any prime number and integer value of finite fields. Also the UMPEU is applied to fault-tolerant computing system, pipeline class. parallel processing network, switching function and its circuits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.147-169
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• 2024

In pace with the development of network technology at lightning speed, social networks have been extensively applied in our lives. However, as social networks retain a large number of users' sensitive information, the openness of this information makes social networks vulnerable to attacks by malicious attackers. To preserve the link privacy of individuals in social networks, an uncertain graph method based on node random response is devised, which satisfies differential privacy while maintaining expected data utility. In this method, to achieve privacy preserving, the random response is applied on nodes to achieve edge modification on an original graph and node differential privacy is introduced to inject uncertainty on the edges. Simultaneously, to keep data utility, a divide and conquer strategy is adopted to decompose the original graph into many sub-graphs and each sub-graph is dealt with separately. In particular, only some larger sub-graphs selected by the exponent mechanism are modified, which further reduces the perturbation to the original graph. The presented method is proven to satisfy differential privacy. The performances of experiments demonstrate that this uncertain graph method can effectively provide a strict privacy guarantee and maintain data utility.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1173-1183
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• 2006

Matrix-star, Pancake, and RFM graphs have such a good property of Star graph and a lower network cost than Hypercube. Matrix-star graph has Star graph as a basic module and the node symmetry, the maximum fault tolerance, and the hierarchical decomposition property. Also it is an interconnection network that improves the network cost against Star graph. In this paper, we propose a method to embed among Matrix-star Pancake, and RFM graphs using the edge definition of graphs. We prove that Matrix-star $MS_{2,n}$ can be embedded into Pancake $P_{2n}$ with dilation 4, expansion 1, and $RFM_{n}$ graphs can be embedded into Pancake $P_{n}$ with dilation 2. Also, we show that Matrix-star $MS_{2,n}$ can be embedded into the $RFM_{2n}$ with average dilation 3.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.147-150
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• 2008

The real road network is regarded as a grid, and the grid is divided by fixed-sized cells. The path-finding is composed of two step searching. First searching travels on the abstract graph which is composed of a set of psuedo vertexes and a set of psuedo edges that are created by real road network and fixed-sized cells. The result of the first searching is a psuedo path which is composed of a set of selected psuedo edges. The cells intersected with the psuedo path are called as valid cells. The second searching travels with $A^*$ algorithm on valid cells. As pruning search space by removing the invalid cells, it would be possible to reduce the cost of exploring on real road network. In this paper, we present the method of creating the abstract graph and propose a path-finding algorithm on the abstract graph for extracting search space before traveling on real road network.

• Journal of KIISE:Information Networking
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• v.32 no.2
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• pp.261-277
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• 2005

We propose the PAD (Power Aware Data-centric Routing Protocol), which finds minimum energy routes and prolongs network life-time, for the data-centric sensor networks. Firstly, the PAD discovers the minimum energy Property graph by removing redundant communication links. The proposed a1gorithm to find the minimum energy property graph is faster, simpler and easier to implement than existing aIgorithms. Secondly, the PAD runs the DDBF (Distributed Data-centric Bellman-Ford Algorithm) to determine routing paths between a base node and all sensor nodes. The DDBF selves the drawbacks of the distributed bellman-ford algorithm, i.e. slow convergence and a possible cyclic routing path. Since the PAD is a fully distributed algorithm with low overhead, it can be used in various kinds of resource limited sensor network applications.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.193-202
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• 2021

The update of a high-precision map was carried out by modifying the geometric information using ortho-images or point-cloud data as the source data and then reconstructing the relationship between the spatial objects. These series of processes take considerable time to process the geometric information, making it difficult to apply real-time route planning to a vehicle quickly. Therefore, this study proposed a method to update the road network for route planning using a graph data structure and storage type of graph data structure considering the characteristics of the road network. The proposed method was also reviewed to assess the feasibility of real-time route information transmission by applying it to actual road data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.567-575
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• 2020

As online social networks are used as a critical medium for modern people's information sharing and relationship, their users are increasing rapidly every year. This not only increases usage but also surpasses the existing media in terms of information credibility. Therefore, emerging marketing strategies are deliberately attacking social networks. As a result, public opinion, which should be formed naturally, is artificially formed by online attacks, and many people trust it. Therefore, many studies have been conducted to detect agents attacking online social networks. In this paper, we analyze the trends of researches attempting to detect such online social network attackers, focusing on researches using social network graph characteristics. While the existing content-based techniques may represent classification errors due to privacy infringement and changes in attack strategies, the graph-based method proposes a more robust detection method using attacker patterns.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.131-140
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• 2009

Recently, the information visualization research community has given significant attention to graph visualization, especially visualization of social networks. However, visualization of information flows in a very large social network has not been studied in depth. However, information flows are tightly related to the structure of social networks and it shows dynamic behavior of interactions between members of social networks. Thus, we can get much useful information about social networks from information flows. In this paper, we present our research result that enables users to navigate a very large social network in Google Maps' method and to take a look at information flows on the network. To this end, we devise three techniques; (i) mapping a very large social network to a 2-dimensional graph layout, (ii) exploring the graph to all directions with zooming it in/out, and (iii) building an efficient query processing framework. With these methods, we can visualize very large social networks and information flows in a limited display area with a limited computing resources.