Earticle

게임 개발은 다른 소프트웨어와 비교해서 다양한 분야의 전문가들이 참여하여 창의적인 과정을 거쳐서 생산물을 만들어낸다. 여기에서 가장 중요시 되고 어려운 분야가 시나리오에 대한 공통되고 동일한 상황 인식이다. 기존의 소프트웨어와 비교해서 게임 개발 방법은 통일된 형태의 개발 및 정형화된 표현 방법이 미흡하다. 본 논문은 시나리오에 대한 개발 참여자들간의 정보를 공유하고 개발 후에 발생할 수 있는 잠재적인 오류 및 개발 비용을 최소화하기 위한 시나리오 표현 방법을 개발한다. 이를 위해 사건-상태-행동 그래프에 기반한 시나리오 표현 방법을 제안한다. 그리고, 게임 시나리오에 대한 충분한 토의와 정보 공유는 게임의 애매하고 창의적인 개발 특성상 유익한 과정이다.. 제안하는 방법을 Age of Empire 게임 시나리오로 표현하여 그 유용성을 보인다.

Design and development of modern computer games can be a complex activity involving many participants from a variety of disciplines. The most important and difficult game's field is a common and identical cognition of game scenario. However, compared to existing types of software, computer games development appears far less formalized. In this paper, we propose a game situation logic design methodology, referred to as Event-State-Action graph, that minimizes the potential error of the game within situation logic and therefore reduces the cost of game development. In addition, the creative and ambiguous process could be greatly beneficial if game scenario ideas were capable of being shared widely and discussed. The suggested purpose and strategy were applied to a typical game, e.g. Age of Empire, and demonstrated the developed method to describe how the system works.

Stock price forecasting is a crucial challenge in FinTech industries, with implications that extend to algorithmic trading. In recent studies, Graph Neural Networks (GNNs) are employed for prediction; however, they are still limited to involving macroeconomic contexts. Hence, in this study, a novel GNN-based method for stock price forecasting is designed, with a graph building structure influenced by macroeconomic variables, namely inflation, interest rate, and GDP growth regimes. Our model captures the relationships between stocks on the basis of regime-specific, macro-driven static graphs along with an LSTM model. The proposed approach outperforms existing methods and provides a new viewpoint on stock forecasting.

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.

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.

Designing a reward function is a critical challenge in reinforcement learning. However, as environments become more complex and tasks grow more difficult, designing a reward function that drives optimal behavior becomes increasingly challenging. To overcome these issues, Preference based reinforcement learning has proposed methods that learn reward functions based on the preference between two trajectories, thereby eliminating the need for handcrafted reward function. In multi-agent reinforcement learning, the challenge is even greater due to the complex interactions among agents, which makes designing a single global reward function even more difficult. In this paper, we show that when a single global reward function is learned via preference-based reinforcement learning in multi-agent setting, it often fails to capture sufficient information for optimal policy learning. Instead, we propose a method for learning individual reward functions that provide additional guidance for each agent’s optimal policy. Our approach, which leverages graph structures and preference-based reinforcement learning, outperforms the method based on learning a single, global reward function.

This study explores the global problem of misinformation dissemination on social media, particularly Twitter, due to the COVID-19 pandemic. It identifies prominent disseminators, investigates the spread of false information and the ecosystem of disinformation spreaders, and assesses their online personalities. We track the interaction among fake news spreaders using the User?User Interaction Graph. The study reveals a rapidly growing population of disseminators, including professional spreaders, with over 3% dominating the others. The collaboration among fake news spreaders is high, highlighting the need for further research using publicly available online data to understand the community spreading malicious misinformation about COVID-19.

In visual robot place recognition (VPR), a scene graph is a rich scene model that can describe the complex contexts in a scene such as the relationships between various types of visual contents including appearance, space, and semantics. However, training an efficient scene graph classifier is not straightforward. Existing approaches typically rely on exhaustive matching between query and database graphs and are not scalable to large-size VPR problems. Our research is motivated by a recent development of the graph convolutional neural network (GCN) as an efficient and discriminative classifier for graph data, and it aims to explore the potential of the GCN as a scene graph classifier. However, unlike several existing GCN applications, no valid scene graph descriptor for a GCN classifier on noisy scene data exists. To address this issue, herein, we propose to train the GCN model in a teacher-to-student knowledge transfer scheme by employing an existing state-of-the-art single-view VPR system as the teacher model. The proposed approach is implemented within a practical VPR framework by combining the best of the following three independent fields: multimodal information retrieval, rank matching, and similarity-based pattern recognition. Experiments using the public NCLT dataset validate the effectiveness of the proposed approach.

To solve the spectrum scarcity problem, dynamic spectrum access (DSA) technology has emerged as a promising solution. Effectively implementing DSA demands accurate and efficient spectrum prediction. However, complex spatiotemporal correlation and heterogeneity in spectrum observations usually make spectral prediction arduous and even ambiguous. In this letter, we propose a spectrum prediction method based on an attention-aided graph convolutional neural network (AttGCN) to capture features in both spatial and temporal dimensions. By leveraging the attention mechanism, the AttGCN adapts its attention weights at different time steps and spatial positions, thus enabling itself to seize changes in spatiotemporal correlations dynamically. Simulation results show that the proposed spectrum prediction method performs better than baseline algorithms in long-term forecasting tasks.

온·오프라인 상태 전환이 빈번하고 대용량 이미지 데이터를 실시간으로 처리해야 하는 드론 운영 환 경에서는, 데이터의 일관성과 충돌 없는 동기화를 보장하는 것이 중요한 과제이다. 본 논문은 소규모 드론 네트워크를 대상으로, Graph Database(그래프 데이터베이스)의 동기화를 위한 Change Data Capture(CDC) 기반 아키텍처를 제안한다. PostgreSQL 기반의 AgensGraph와 Debezium, Apache Kafka를 연동하여, 오프라인 상태에서 발생한 변경 사항을 안정적으로 캡처·버퍼링하고 온라인 복귀 시 정렬된 순서대로 전파함으로써 데이터의 무결성과 일관성을 확보한다. 본 연구의 핵심은 그래프 구조 메타데이터(노드 및 엣지 타입 등)와 운영 데이터(예: 이미지 태그, 객체 임베딩)를 분리하여 관 리함으로써, 충돌 상황에 유연하게 대응하고 구조 변경의 일관성을 유지할 수 있도록 설계한 것이다. 또한, 그래프 구조 변경은 마스터 노드를 통해서만 수행하도록 제한함으로써 분산 노드 간의 충돌을 사전에 방지한다. 각 이벤트에는 버전 정보와 WAL 기반 LSN(Log Sequence Number)을 포함시켜 중복 적용을 방지하고, 재처리 상황에서도 순차성이 보장되도록 하였다. 최대 200~300대 수준의 드론 노드를 대상으로 한 시뮬레이션을 통해 본 시스템의 확장성, 저지연 동기화, 충돌 회피 효과를 확인 하였으며, 제한된 연결 환경에서도 실시간 AI 학습, 협업 및 의사결정 지원이 가능한 유연한 그래프 기반 데이터 계층 모델임을 입증하였다.

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.

Owing to the hyper-connectivity of convergence environments, the Internet of Blended Environments (IoBE) has emerged As a result, the environments and architectures in which cyber-security threats can occur have steadily diversified leading to an increase in security incidents. However, existing detection systems lack correlation analysis and thus cannot detect the corresponding diverse attack paths and attack chains effectively. In this paper, we propose a data prediction technique in which knowledge graph embedding technology is applied to predict blended threats in complex environments such as the IoBE. We also verify the performance of the proposed technique.

Distributed handover (HO) strategy with low complexity can provide seamless communication in low earth orbit (LEO) satellite networks. However, it is difficult to consider load balancing in distributed HO strategy, which may results in HO failures. In this paper, we propose a graph neural network (GNN) based distributed HO strategy for LEO satellite communication to maximize sum rate by considering load balancing. We first propose target satellite selection method with GNN where each user equipment (UE) selects target satellite and requests HO to it. We then employ ACK decision policy to strictly satisfy load balancing of satellites where each satellite decides HO requests from UEs depending on its load condition. To validate the proposed GNN based HO, we use the System Tool Kit (STK) for modeling LEO satellites with 22 orbits and 72 satellites are in each orbit, and evaluate the HO process during 2400 s. From this constellation, we generate 9,600 samples by randomly deploying UEs on the ground and use them as dataset. Simulation results show that the proposed GNN based HO strategy outperforms conventional HO strategies by selecting an appropriate target satellite. We also demonstrate that load balancing is satisfied due to ACK decision policy and the scalability of proposed GNN architecture is ensured with different network sizes.

The size of the maximum independent set (MIS) in a graph G is called the independence number. The size of the minimum connected dominating set (MIN-CDS) in G is called the connected domination number. The aim of this paper is to determine two better upper bounds of the independence number; dependent on the connected domination number for a unit ball graph. Further, we improve the upper bound to obtain the best bound with respect to the upper bounds obtained thus far.

Objective To investigate the global functional reorganization of the brain following spinal cord injury with graph theory based approach by creating whole brain functional connectivity networks from resting state-functional magnetic resonance imaging (rs-fMRI), characterizing the reorganization of these networks using graph theoretical metrics and to compare these metrics between patients with spinal cord injury (SCI) and age-matched controls.Methods Twenty patients with incomplete cervical SCI (14 males, 6 females; age, 55±14.1 years) and 20 healthy subjects (10 males, 10 females; age, 52.9±13.6 years) participated in this study. To analyze the characteristics of the whole brain network constructed with functional connectivity using rs-fMRI, graph theoretical measures were calculated including clustering coefficient, characteristic path length, global efficiency and small-worldness. Results Clustering coefficient, global efficiency and small-worldness did not show any difference between controls and SCIs in all density ranges. The normalized characteristic path length to random network was higher in SCI patients than in controls and reached statistical significance at 12%?13% of density (p<0.05, uncorrected).Conclusion The graph theoretical approach in brain functional connectivity might be helpful to reveal the information processing after SCI. These findings imply that patients with SCI can build on preserved competent brain control. Further analyses, such as topological rearrangement and hub region identification, will be needed for better understanding of neuroplasticity in patients with SCI.

Identity-aware activity recognition is a key enabler for customized services. However, joint modeling of activity recognition and user identification from wireless signals remains underexplored. This work presents a dual-task graph model for millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radar point-cloud sequences. We construct directed graphs that capture a user’s spatial structure and motion over time. A shared graph neural backbone processes these graphs and produces node embeddings that encode local spatial features and short-term dynamics. Each task-specific head first aggregates node embeddings into a graph-level representation and then performs activity or identity classification. Experiments on two public datasets demonstrate that the proposed scheme achieves classification performance comparable to single-task baselines for both activity recognition and user identification while maintaining low-latency inference. Codes are available at?https://github.com/junyongeom/mmActId/.

In this letter, we are the first to focus on the issue of reliable and flexible radio resource allocation (RRA) for beam-hopping (BH) in satellite internet network (SIN). The main new challenges are accurate and dynamic radio resource modeling and high-efficiency RRA in high-dynamic scenarios. Therefore, we propose a novel RRA scheme for BH-SIN based on graph mapping and generative adversarial network (GAN). In our scheme, the characteristics of radio resources are first to be converted to graphical features. Then, the former RRA schemes are modeled as one of the adversarial objects, to adaptively optimize the next best solution to the changeable scenarios and situations. The simulation results show that the proposed RRA schemes improve the throughput and quality of service by 15% and 22%. 2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

This study develops a systematic, verifiable experimental study to clarify when and how transforming tabular data into graph structures enhances node-level classification: AI-generated synthetic dataset with controlled numbers of nodes, classes, and imbalance; two interpretable graphs are constructed (1) a similarity-based k-nearest neighbors graph with the number of neighbors and model depth varied, and (2) a rule-based graph with explicit, transparent connection rules; and graph-based methods GCN, GraphSAGE to approach this baseline when k and depth are appropriately tuned, before performance saturates or declines due to excessive signal averaging; and rule-based graphs expose architectural differences GraphSAGE is higherperforming and more stable, whereas GCN is more structuresensitive and degrades with depth implying that approaches preserving node-specific information and flexibly aggregating signals are more robust to structural heterogeneity. Overall, the framework offers practical guidance for method selection and graph construction particularly the choice neighbors and depth in a simplified, reproducible form readily extensible to real-world applications.

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.

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.

1990년대 초반 데이터마이닝 방법론이 등장한 이후 현재까지 정확성과 신속성을 향상시키기 위한 기법들이 꾸준히 연구되고 있다. 본 연구에서는 데이터마이닝 방법론 중 하나인 연관규칙 마이닝 과정에서 작업 수행 시간을 최소화하기 위하여 그래프 구조를 사용하였다. 본 연구에서 제시한 알고리즘의 특징은 다음과 같다. 첫째, 데이터베이스 스캐닝을 하여 연관규칙을 찾는데 필요한 모든 정보를 그래프에 저장하였다. 둘째, 트랜잭션 그룹핑을 통해 동일한 레코드 값에 대한 접근 횟수를 최소화하였다. 셋째, 동일한 데이터에서 파라메터의 값만 변경하여 새로 연관규칙을 찾으려 하는 경우, 기 생성된 그래프의 데이터를 재사용함으로써 연관규칙을 찾는 시간을 최소화하였다. 넷째, 빈발항목집합을 찾는 과정에서 후보항목집합의 생성을 배제하였다. 마지막으로, 알고리즘의 성능을 측정하기 위하여 전통적인 연관규칙분석 방법인 Apriori Algorithm과 메모리 기반의 데이터 클러스터링 방법을 사용한 Cluster-based Association Rule Algorithm을 구현하여 비교한 결과, 본 연구에서 제시한 알고리즘이 기존 연구보다 효율성이 높게 나타남을 확인하였다.