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

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.65-67
• /
• 2004

Change of state of Channel between two wireless terminals which is caused by noise and multiple environmental conditions for happens frequently from the Wireles Network. So, When it is like that planning a wireless network protocol or performance analysis, it follows to change of state of time-varying channel and packet the analysis against a transmission efficiency is necessary. In this paper, analyzes transmission time of a packet and a packet in a time-varying and packet based Wireless Network. To reflecte the feature of the time-varying channel, we use a Signal Flow Graph model. From the model the mean of transmission time and the mean of queue length of the packet are analyzed in terms of the packet distribution function, the packet transmission service time, and the PER of the time-varying channel.

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

• Journal of information and communication convergence engineering
• /
• 제6권4호
• /
• pp.470-474
• /
• 2008

A well-designed computer network technology produces benefits on several fields within the organization, between the organizations(suborganizations) or among different organizations(suborganizations). Network technology streamlines business processes, decision process. Graphs are useful data structures capable of efficiently representing a variety of networks in the various fields. Metagraph is a like graph theoretic construct introduced recently by Basu and Blanning in which there is set to set mapping in place of node to node as in a conventional graph structure. Metagraph is thus a new type of data structure occupying its popularity among the computer scientists very fast. Every graph is special case of Metagraph. In this paper the authors introduce the notion of Meta-Networking as a new network technological representation, which is having all the capabilities of crisp network as well as few additional capabilities. It is expected that the notion of meta-networking will have huge applications in due course. This paper will play the role of introducing this new concept to the network technologists and scientists.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제9권8호
• /
• pp.243-250
• /
• 2020

지식 그래프 기반의 질문 응답 문제는 자연어 질문들에 대한 깊은 이해뿐만 아니라, 대규모 지식 그래프 상에서 올바른 답변을 찾기 위한 효과적인 추론 능력을 필요로 한다. 본 논문에서는 다중 홉 추론을 요구하는 복잡한 자연어 질문에 대해 연관 지식 그래프 위에서 답변 추론을 효과적으로 수행할 수 있는 심층 신경망 모델을 제안한다. 제안 모델에서는 지식 그래프 상의 각 개체 노드와 이웃 노드 간의 양방향 특징 전파를 허용할뿐만 아니라, 두 이웃 노드 쌍 간의 맥락 정보까지 활용할 수 있는, 표현력이 뛰어난 쌍 선형 그래프 신경망(BGNN)을 이용한다. 본 논문에서는 오픈 도메인의 지식 베이스인 Freebase, 자연어 질문 응답을 위한 벤치마크 데이터 집합들인 WebQuestionsSP와 MetaQA를 이용한 실험들을 통해, 제안 모델의 효과와 우수성을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권5호
• /
• pp.1909-1928
• /
• 2020

Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.

• 반도체디스플레이기술학회지
• /
• 제20권3호
• /
• pp.113-116
• /
• 2021

As artificial intelligence technologies, including deep learning, develop, these technologies are being introduced to code similarity analysis. In the traditional analysis method of calculating the graph edit distance (GED) after converting the source code into a control flow graph (CFG), there are studies that calculate the GED through a trained graph neural network (GNN) with the converted CFG, Methods for analyzing code similarity through CNN by imaging CFG are also being studied. In this paper, to determine which approach will be effective and efficient in researching code similarity analysis methods using artificial intelligence in the future, code similarity is measured through funcGNN, which measures code similarity using GNN, and Siamese Network, which is an image similarity analysis model. The accuracy was compared and analyzed. As a result of the analysis, the error rate (0.0458) of the Siamese network was bigger than that of the funcGNN (0.0362).

• 한국정보처리학회논문지
• /
• 제5권10호
• /
• pp.2615-2626
• /
• 1998

본 논문에서는 방향성 그래프인 Rotator 그래프와 Faber-Moore 그래프를 병합한 새로운 상호 연결망으로 RFM그래프를 제안하고, RFM그래프에 대하여 고장 허용도, 라우팅 알고리즘, 노드 중복 없는 싸이클, 방송 알고리즘을 분석한다. 또한 2차원 토러스, Strar 그래프, Bubblesort 그래프를 RFM 그래프에 연장비율(dilation) 2와 확장비율(expansion)1에 임베딩하는 방법을 제시한다.

• 대한전자공학회논문지
• /
• 제21권2호
• /
• pp.19-23
• /
• 1984

새로운 방향성 적선도(directed product graph; DPG)를 제안하고 적선도에 가지의 방향성과 그 환로의 개념을 도입하므로 상위수학적으로 능동과 또한 결합성 소자까지 포함하는 회로에 대한 Mason공식의 분모(△)항을 그 부호와 소거항에 무관하게 보다 쉽게 구하게 하였다. 또한 이때 회로망 선도에서 나무(tree)를 선택하는데 따르는 제약조건을 제거하였다.

• Journal of Information Processing Systems
• /
• 제19권6호
• /
• pp.803-816
• /
• 2023

Explainable artificial intelligence is a method that explains how a complex model (e.g., a deep neural network) yields its output from a given input. Recently, graph-type data have been widely used in various fields, and diverse graph neural networks (GNNs) have been developed for graph-type data. However, methods to explain the behavior of GNNs have not been studied much, and only a limited understanding of GNNs is currently available. Therefore, in this paper, we propose an explanation method for node classification using graph convolutional networks (GCNs), which is a representative type of GNN. The proposed method finds out which features of each node have the greatest influence on the classification of that node using GCN. The proposed method identifies influential features by backtracking the layers of the GCN from the output layer to the input layer using the gradients. The experimental results on both synthetic and real datasets demonstrate that the proposed explanation method accurately identifies the features of each node that have the greatest influence on its classification.