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Social bot detection on emerging platforms is constrained by sparse labels and by evaluation practices that focus on classification accuracy while ignoring network validity for network analytics. This study proposes a graph-based semi-supervised bot detection framework for Bluesky. From 2.2 million accounts, we construct 6,891 high-confidence bot seeds and 683,209 conservative non-bot anchors without treating unlabeled accounts as humans. We then introduce a Role-Discrepancy GNN that models directional role asymmetry across repost, reply, quote, and mention relations. On a temporal test split, the proposed model achieves the best overall performance among profile-only and graph-based baselines, with AUC = 0.9930 and F1 = 0.8936. Network validation further shows the strongest structural separation, with modularity Q = 0.0547 and mean homophily gap ΔH = 0.0515. These results support relation-role discrepancy as a useful signal for bot detection under sparse labels.

병원 간 전자의무기록 (EHR) 시스템은 중앙 집중식 학습 최적화를 제한하는 개인정보 보호 및 업무량 다양성 문제에 직면해 있다. 본 논문에서는 기관의 자율성을 유지하면서 비용 예측 및 쿼리 실행 효율성을 향상시키는 연합 쿼리 최적화 프레임워크를 구축하고자 한다. 계획 그래프 표현, GNN 기반 비용 학습, 그리고 병원 간 연합 집계 기능을 결합한 연합 그래프 기반 쿼리 최적화 모델인 FedGQO를 제안한다. FedGQO는 원시 EHR 데이터나 상세한 로컬 쿼리 로그를 공유하지 않고도 협업 학습을 가능하게 하며, 비독립 동일분포(non-IID) 워크로드 하에서도 견고함을 유지한다. 실험 결과, FedGQO는 MSE 8.98, 평균 q-오차 1.21, 평균 실행 시간 8.40을 달성하여, 중앙 집중식 상한선에 근접하면서도 기본 최적화기 및 Local-GNN을 능가하는 성능을 보였다. 이러한 결과는 FedGQO가 병원 간 전자의무기록 쿼리 최적화를 위한 효과적이고 개인정보를 보호하는 솔루션임을 입증한다.

Cross-hospital EHR systems face privacy and workload diversity challenges that limit centralized learned optimization. We aim to build a federated query optimization framework that improves cost prediction and query execution efficiency while preserving institutional autonomy. We propose FedGQO, a federated graph-based query optimization model that combines plan-graph representation, GNN-based cost learning, and federated aggregation across hospitals. FedGQO enables collaborative training without sharing raw EHR data or detailed local query logs, while remaining robust under non-IID workloads. In experiments, it achieved an MSE of 8.98, a mean q-error of 1.21, and an average runtime of 8.40, outperforming the native optimizer and Local-GNN while remaining close to the centralized upper bound. These results show that FedGQO is an effective and privacy-preserving solution for cross-hospital EHR query optimization.

Predicting accurate human migration patterns is crucial for effective urban planning. However, accurate human migration patterns prediction remains a challenging task. Existing methods, such as Graph Neural Network approaches, often overlook dynamic temporal variations and directional dependencies in large-scale migration data. To overcome this challenge, we propose MiGA-Net (Migration Graph Attention Network), a graph Neural network-based framework enhanced with an attention mechanism to capture complex spatiotemporal dependencies and highlight significant migration flows on the domestic and international level. We utilize two different datasets of the Shinan-gun, South Korea, for international and domestic regions. Experimental results show that the proposed MiGA-Net achieved superior performance over both datasets. The model achieved 0.0027 MAE for domestic flow and 0.0155 for international flow, demonstrating the effectiveness of the proposed framework.

The rise of wireless devices makes interference a key challenge for reliable communication in dense spectrum-sharing networks. This paper proposes a graph neural network (GNN)-based power control algorithm to minimize the worst-case outage probability by using statistical channel state information (CSI), i.e., position information. By representing the network as a fully connected directed graph with node and edge features derived from transceiver positions, the GNN employs message-passing layers to aggregate interference patterns and infer near-optimal transmit powers. Simulation results demonstrate the scalability and generalization capability of the proposed method, confirming its suitability for real-time deployment in large-scale wireless systems.

최근 활발히 연구되는 딥러닝 방법론은 인공지능의 성능을 급속도로 향상시켰고, 이에 따라 다양한 산업 분야에서 딥러닝을 활용한 시스템이 제시되고 있다. 교통 시스템에서는 GNN을 활용한 공간-시간 그래프 모델링이 교통 속도 예측에 효과적인 것으로 밝혀졌지만, 이는 메모 리 병목 현상을 유발하기 때문에 모델이 비효율적으로 학습된다는 단점이 있다. 따라서 본 연 구에서는 그래프 분할 방법을 통해 도로 네트워크를 분할하여 메모리 병목 현상을 완화함과 동시에 우수한 성능을 달성하고자 한다. 제안 방법론을 검증하기 위해 인천시 UTIC 데이터 분석 결과를 바탕으로 Jensen-Shannon divergence를 사용하여 도로 속도 분포의 유사도를 측정 하였다. 그리고 측정된 유사도를 바탕으로 스펙트럴 클러스터링을 수행하여 도로 네트워크를 군집화하였다. 성능 측정 결과, 도로 네트워크가 7개의 네트워크로 분할되었을 때 MAE 기준 5.52km/h의 오차로 비교 모델 대비 가장 우수한 정확도를 보임과 동시에 메모리 병목 현상 또 한 완화되는 것을 확인할 수 있었다.

Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.

As urban networks integrate heterogeneous radio access technologies (RATs), such as Wi-Fi and 5G/B5G, modeling performance becomes challenging due to interference, spatial variability, and propagation conditions. This paper proposes a graph neural network (GNN)-based framework for predicting throughput, delay, and jitter in multi-RAT environments, considering RAT type. The model encodes network topology and channel characteristics using node and edge features, capturing spatial configuration, congestion, and line-of-sight (LoS) versus non-line-of-sight (NLoS) conditions. The results show that GNNs exhibit robustness across station densities and spatial conditions. The message-passing GNN method performs well for throughput and delay, while non-graph methods better estimate jitter.

Late blight, caused by Phytophthora infestans, threatens tomato and potato crops in Nepal. This study explores developing and deploying a mobile application powered by a graph neural network (GNN) to predict late blight risk for Nepali farmers. The GNN trained on 43 years of NASA satellite weather data can generate 5-days forecast for 320 weather stations in Sudurpashim and Karnali Province, Nepal. The mobile application offers user-friendly forecasts and visualizes late blight risk through clear graphical interfaces. In the visited sites, 30% of tomato and potato crops were found infected with late blight, which the app had identified as high-risk. Samples infected with late blight were collected and analyzed in a wet lab setting. All infected samples tested positive for P. infestans, confirming the app's ability to predict real-world late blight outbreaks. This study showcases the successful development and deployment of a GNN-powered mobile application for assessing late blight risk in Nepal. The application disseminates critical weather information and localized risk assessments, potentially enhancing late blight management in tomato and potato crops. Further research, including extensive field trials comparing with farmers' practices, could increase the application's usability in Nepali fields.

Our research aims to enhance the modeling of speech signals for more effective extraction of node features and analysis of relationships between nodes. To achieve this, we model speech signals as cyclic or linear graphs. Our model combines layers of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to leverage their respective strengths in processing graph data. Specifically, we utilize GCN to aggregate information from neighboring nodes, which helps capture local relationships among nodes. Additionally, we employ GAT mechanisms to assign varying attention weights to different neighboring nodes, facilitating a better capture of complex global relationships between nodes. In our experiments, we validate our approach using the IEMOCAP dataset and demonstrate comparable performance to state-ofthe- art models in emotion recognition tasks. This research outcome provides new insights and methodologies for further exploration in the field of speech signal processing.

Recently, a graph neural network has played a crucial role across various fields. In this paper, we designed a Graph Convolutional Network (GCN) to analyze population movement at the city level. It consists of four Graph Convolution (GC) layers, with each layer responsible for aggregating knowledge from its neighboring nodes and updating the feature representation for each city. We utilized population mobility data from China, which includes daily city-to-city movement data. GCN estimates the strength of relationships among all cities. Experimental results demonstrate that the proposed GCN achieves improved performance in estimating city-to-city migration flow relationships.

The rapid growth of e-commerce platforms demands recommendation systems that balance scalability with representational expressiveness. While Matrix Factorization (MF)-based collaborative filtering offers computational efficiency, it fails to capture high-order contextual relationships among items. Graph Neural Networks (GNNs) address this limitation but incur prohibitive computational costs in large-scale, real-time settings. To resolve this tradeoff, we propose a unified hybrid framework integrating MFbased candidate generation and GNN-based re-ranking within a retrieve–then–rank architecture. The MF stage efficiently generates a candidate set, while the GNN stage refines rankings via a heterogeneous graph incorporating user–item interactions, item attributes, and category structures. We further introduce an availability-aware scoring mechanism that integrates real-time stock information to enhance practical applicability. Extensive experiments on a large-scale e-commerce dataset demonstrate that the proposed framework consistently outperforms baseline methods, effectively mitigates data sparsity, and maintains computational efficiency suitable for real-world deployment.

In recent years, the anticipation of human mobility flow has significant applications in various domains ranging from urban planning to public health. This study proposes a hybrid Graph Neural Network and Long Short Term- Memory network-based model for nationwide human mobility prediction, effectively capturing inter-urban movement patterns. We validate the feasibility and effectiveness of our model using the Korean internal-city mobility dataset, which captures real-world population movement patterns across various urban regions. Our experimental results accurately predict inter-city mobility, advancing urban planning, health, and transport.

기업 부실 예측 모델은 부도와 같은 사후적 사건에 집중하여, 그 전 단계의 중요한 위험 신호인 비적정 감사의견을 예측하는 데 한계가 있었습니다. 기업 간 상호작용으로 전파되는 네트워크 리스크를 포착하지 못하는 한계가 있었다. 본 연구는 이러한 한계를 극복하고자 그래프 신경망(GNN)을 활용하여 기업의 구조적 연결성을 반영한 비적정 감사의견 예측 모델을 제안한다. 금융감독원 DART 공시 데이터를 기반으로 2,057개 상장기업의 5년간 재무 정보와 감사의견을 수집하고, 기업 간 출자 관계와 감사인 공유 관계를 통합하여 약 45만 건의 엣지로 구성된 이중 관계 네트워크(Dual-edge Network)를 구축하였다. 기업의 잠재적 부실 징후는 외부감사인의 비적정 감사의견을 기준으로 정의하였으며, GCN, GraphSAGE, GATv2 등 다양한 GNN 모델을 적용하여 로지스틱 회귀 및 XGBoost와 같은 전통적 모델과 성능을 비교 평가하였다. 실험 결과, 하이퍼파라미터 튜닝을 거친 GraphSAGE 모델이 비적정 감사의견 기업(소수 클래스) 탐지 성능에서 F1-Score 0.551과 ROC-AUC 0.936을 기록하며 종합적으로 가장 안정적인 예측력을 보였다. 이는 기업 네트워크 내 위험 신호를 효과적으로 일반화하여 학습하는 능력의 중요성을 입증한다. GNNExplainer를 통한 분석은 모델 예측의 해석 가능성을 제시했으며, 본 연구 결과는 금융기관, 투자자, 감사인이 기업의 감사 리스크(Audit Risk)를 사전에 식별하는 조기 경보 시스템으로 활용되어 실무적으로 기여할 수 있다. 향후 시계열 데이터를 반영한 동적 그래프 모델링 및 공급망 등 다양한 관계를 통합하는 연구로 확장될 수 있을 것이다.

Traditional models for predicting corporate failure or audit risk have relied heavily on the financial indicators of individual firms, failing to capture network risks that propagate through inter-firm interactions. To address this limitation, this study proposes an audit risk prediction model that incorporates structural connectivity among companies using Graph Neural Networks (GNNs). Based on public disclosure data from the Financial Supervisory Service’s DART system, we collected five years of financial information and audit opinions for 2,057 listed companies. By integrating equity investment relationships and shared auditor connections, we constructed a dual-edge network consisting of approximately 450,000 edges. Audit risk was defined according to external audit opinions, and various GNN models—including GCN, GraphSAGE, and GATv2—were evaluated and compared with traditional models such as logistic regression and XGBoost. Experimental results show that the hyperparameter-tuned GraphSAGE model achieved the most stable and comprehensive predictive performance in detecting at-risk firms (minority class), with an F1-Score of 0.551 and an ROC-AUC of 0.936. This highlights the importance of effectively learning and generalizing risk signals embedded within corporate networks. Moreover, analysis using GNNExplainer provided interpretability into the model’s predictions. The findings of this study offer practical implications for improving audit risk management systems and advancing network-based risk management in financial institutions. Future research can be extended to dynamic graph modeling by incorporating time-series data and integrating additional relational dimensions such as supply chain links.

본 논문은 음성신호를 순환 또는 선형그래프로 모델링하여 노드 특성 추출 및 효과적인 노드 간 관계 분석을 기반으 로 하는 음성감정인식 방법을 제안한다. 제안한 방법은 그래프 데이터를 처리하는 데 있어 각각의 강점을 활용하기 위해 그래프 합성곱 신경망(GCN)과 그래프 어텐션 신경망(GAT) 계층을 결합한다. 구체적으로, GCN을 사용하여 이웃 노드로부터 정보를 집계함으로써 노드 간의 로컬 관계를 포착한다. 또한, GAT 메커니즘을 활용하여 다양한 이웃 노드에 가중치를 부여하여 노드 간의 복잡한 글로벌 관계를 더 잘 포착할 수 있도록 한다. 실험에서는 IEMOCAP 데이터셋을 사용하여 우리의 접근 방식을 검증하고, 감정 인식 작업에서 최신 모델과 견줄 만한 성능을 보여준다. 이 연구 결과는 음성 신호 처리 분야에서 추가 탐구를 위한 새로운 통찰력과 방법론을 제공한다.

This paper proposes a speech emotion recognition method based on modeling speech signals as circular or linear graphs, enabling the extraction of node characteristics and practical analysis of relationships between nodes. The proposed method combines Graph Convolutional Networks(GCN) and Graph Attention Networks(GAT) layers to leverage the strengths of each in processing graph data. Precisely, GCN captures local relationships between nodes by aggregating information from neighboring nodes. The GAT mechanism better captures complex global relationships between nodes by assigning weights to neighboring nodes. Experiments validate our approach using the IEMOCAP dataset, demonstrating performance comparable to state-of-the-art models in emotion recognition tasks. The results of this study provide new insights and methodologies for further exploration in the field of speech signal processing.

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

본 논문은 그래프 합성곱 신경망을 이용한 신경망 구조 탐색 모델 설계를 제안한다. 딥 러닝은 블랙박스로 학 습이 진행되는 특성으로 인해 설계한 모델이 최적화된 성능을 가지는 구조인지 검증하지 못하는 문제점이 존재한다. 신경망 구조 탐색 모델은 모델을 생성하는 순환 신경망과 생성된 네트워크인 합성곱 신경망으로 구성되어있다. 통상 의 신경망 구조 탐색 모델은 순환신경망 계열을 사용하지만 우리는 본 논문에서 순환신경망 대신 그래프 합성곱 신 경망을 사용하여 합성곱 신경망 모델을 생성하는 GC-NAS를 제안한다. 제안하는 GC-NAS는 Layer Extraction Block을 이용하여 Depth를 탐색하며 Hyper Parameter Prediction Block을 이용하여 Depth 정보를 기반으로 한 spatial, temporal 정보(hyper parameter)를 병렬적으로 탐색합니다. 따라서 Depth 정보를 반영하기 때문에 탐색 영 역이 더 넓으며 Depth 정보와 병렬적 탐색을 진행함으로 모델의 탐색 영역의 목적성이 분명하기 때문에 GC-NAS대 비 이론적 구조에 있어서 우위에 있다고 판단된다. GC-NAS는 그래프 합성곱 신경망 블록 및 그래프 생성 알고리즘 을 통하여 기존 신경망 구조 탐색 모델에서 순환 신경망이 가지는 고차원 시간 축의 문제와 공간적 탐색의 범위 문 제를 해결할 것으로 기대한다. 또한 우리는 본 논문이 제안하는 GC-NAS를 통하여 신경망 구조 탐색에 그래프 합성 곱 신경망을 적용하는 연구가 활발히 이루어질 수 있는 계기가 될 수 있기를 기대한다.

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.

본 논문은 챕터 수준의 강의 동영상 추천 시스템에 있어서 추천의 정확도와 처리속도 간의 균형문제, 즉, 추천 정확도를 향상시키려면 처리 속도가 저하되고, 반대로 처리 속도를 높일 경우 정확도가 감소하는 문제에 대하여 연구한다. 본 논문에서는 이의 해결을 위하여 TF-IDF, K-Means++ Clustering, Graph Neural Network(GNN) 등 다양한 기법을 복합적으로 활용하는 방법을 제안한다. 즉, 챕터들의 유사성을 바탕으로 클러스터를 사전에 구성함으로써 검색 시의 계산량을 줄여 속도를 향상시키면서도, 클러스터를 노드로 하는 그래프에 대하여 GNN을 적용함으로써 추천의 정확도를 향상시키는 방법을 제안한다. 실험 결과 GNN을 사용한 경우 추천의 정확도가 MRR 지표에서 약 19.7% 증가하였으며, 유사도 기반의 정밀도에 있어서 약 27.7% 증가하는 결과를 확인할 수 있었다. 이를 통해 학습자의 질의에 보다 적합한 동영상 챕터를 추천하는 학습시스템 구축에 기여할 것으로 기대한다.

In this paper, we propose a system for recommending lecture videos at the chapter level, addressing the balance between accuracy and processing speed in chapter-level video recommendations. Specifically, it has been observed that enhancing recommendation accuracy reduces processing speed, while increasing processing speed decreases accuracy. To mitigate this trade-off, a hybrid approach is proposed, utilizing techniques such as TF-IDF, k-means++ clustering, and Graph Neural Networks (GNN). The approach involves pre-constructing clusters based on chapter similarity to reduce computational load during recommendations, thereby improving processing speed, and applying GNN to the graph of clusters as nodes to enhance recommendation accuracy. Experimental results indicate that the use of GNN resulted in an approximate 19.7% increase in recommendation accuracy, as measured by the Mean Reciprocal Rank (MRR) metric, and an approximate 27.7% increase in precision defined by similarities. These findings are expected to contribute to the development of a learning system that recommends more suitable video chapters in response to learners' queries.

현대 도시의 복잡한 물리적 구조로 인해 발생하는 다양한 환경 문제에 대응하기 위해서는 그 공간적 특성을 정량적으로 이해하고 유형화하는 것이 필수적이다. 이에 본 연구는 기존의 규칙 기반 도시 공간 분류 체계가 지닌 공간적 관계성 부재의 한계를 극복하고자, 그래프 신경망(Graph Neural Networks, GNN)과 자기지도학습(Self-Supervised Learning, SSL)을 결합한 새로운 도시 공간 유형화 방법론을 제시한다. 서울특별시를 대상으로 다차원적인 도시 공간 변수를 활용하여 공간 그래프를 구축하였다. GNN 모델을 자기지도학습 프레임워크인 Deep Graph Infomax(DGI)로 학습시켜 각 공간 단위의 고유한 특성과 주변과의 관계를 효과적으로 표현하는 임베딩 벡터를 추출했다. 최종적으로 K-means 군집 분석을 통해 서울의 도시 공간을 9개의 유형으로 분류하였으며, 이를 다시 '고밀도 도시화 지역', '녹지 및 오픈스페이스 지역', '교통 통행 집중 지역'이라는 세 가지 대분류로 해석하였다. 본 연구는 도시 공간을 상호 연결된 네트워크로 분석함으로써, 눈에 보이지 않는 도시의 복잡한 기능적 연관성을 데이터 기반으로 규명했다는 점에서 의의가 있다. 연구 결과는 향후 정교한 도시 계획 및 환경 정책 수립을 위한 공간범위 설정의 기초자료로 활용될 수 있을 것으로 기대한다.

Modern cities face diverse environmental challenges driven by their complex physical structures, making it essential to quantitatively analyze and classify spatial characteristics. This study introduces a new urban spatial typology combining Graph Neural Networks(GNN) and Self-Supervised Learning(SSL) to address the lack of spatial relationality in rule-based classification systems. Using multidimensional spatial variables, a graph representation of Seoul was built and trained through the Deep Graph Infomax(DGI) framework to generate embeddings that capture both local features and spatial context. K-means clustering identified nine urban types, further grouped into three categories: High-density Urbanized Areas, Green and Open Spaces, and Traffic Concentration Areas. By analyzing urban space as an interconnected network, this study identifies the complex and often invisible functional relationships within the city based on data. The findings are expected to serve as foundational data for defining spatial boundaries in future urban planning and environmental policy development.

5G 및 B5G(Beyond 5G) 네트워크의 등장으로 기존 네트워크 한계를 극복할 수 있는 네트워크 가상화 기술이 주목받고 있다. 네트워크 가상화의 목적은 효율적 네트워크 자원의 활용과 다양한 전송요구 서비스에 대한 솔루션을 제공하기 위함이다. 이와 관련하여 여러 가지 휴리스틱 기반의 VNE 기법이 연구되고 있으나 네트워크 자원할당 및 서비스의 유연성이 제한되는 문제점을 지니고 있다. 본 논문에서는 다양한 응용의 서비스 요구사항을 충족하기 위해 GNN 기반의 네트워크 슬라이싱 분류 기법과 최적의 자원할당을 위한 RL 기반 VNE 기법을 제안한다. 제안된 기법에서는 Actor-Critic 네트워크를 이용하여 최적의 VNE를 수행한다. 또한 성능 평가를 위해 제안된 기법과 기존의 Node Rank, MCST-VNE, GCN-VNE 기법과의 성능을 비교분석하고 서비스 수용률 제고 및 효율적 자원 할당 측면에서 성능이 향상됨을 보인다.

With the advent of 5G and B5G (Beyond 5G) networks, network virtualization technology that can overcome the limitations of existing networks is attracting attention. The purpose of network virtualization is to provide solutions for efficient network resource utilization and various services. Existing heuristic-based VNE (Virtual Network Embedding) techniques have been studied, but the flexibility is limited. Therefore, in this paper, we propose a GNN-based network slicing classification scheme to meet various service requirements and a RL-based VNE scheme for optimal resource allocation. The proposed method performs optimal VNE using an Actor-Critic network. Finally, to evaluate the performance of the proposed technique, we compare it with Node Rank, MCST-VNE, and GCN-VNE techniques. Through performance analysis, it was shown that the GNN and RL-based VNE techniques are better than the existing techniques in terms of acceptance rate and resource efficiency.