Earticle

In this study, we investigated graph types and characteristics used in Korean science textbooks. For thispurpose, we find the graphs and analysed the graph types in the 16 science textbooks. As the results,we found the more graphs in the higher grade. And 'earth science' used the more graph than others.Line graph and contour map was the most widely used graph type in science textbook and simple linegraph was the most used graph in the line graph.

Bayesian Personalized Ranking (BPR) assigns ranks to a set of items to recommend them to users. This study proposes a novel approach to improve the performance of recommendation systems. The first proposed method for the enhanced recommendation system constructs positive preferences by utilizing only the items explicitly preferred by users rather than treating all interacted items (e.g., clicked, rated, or reviewed) as positive, as was traditionally done. The second method involves using explicitly non-preferred items as negative data, and the traditional approach of using only noninteracted items as negative. Message propagation is performed on subgraphs generated through meta-path design. The results of each subgraph are used to learn the representations of users and items through an attention mechanism and graph representation learning based on this data configuration. The system then predicts scores for user-item pairs. For evaluation, the performance of the recommendation system was assessed using not only traditional accuracy metrics but also by defining a pairwise ranking accuracy metric. Pairwise ranking accuracy assigns ranks to preferred and non-preferred items to determine if the model reflects user preferences. Experimental results showed improved performance in widely used evaluation metrics for recommendation systems, such as Hit Rate and Normalized Discounted Cumulative Gain, and higher performance in pairwise ranking accuracy.

본 연구는 수학과 교육과정에서 막대그래프와 꺾은선그래프를 학습한 초등학교 4, 5, 6학년 학생들의 혼합그래프에 대한 이해 능력을 분석하는 것을 목적으로 한다. 이를 위해 단일그래프와 혼합그래프에 대한 이해 능력을 조사하는 검사지를 제작 하여 총 226명의 학생들에게 적용하였다. 분석 결과, 모든 학년에서 혼합그래프의 정답률이 단일그래프에 비해 낮게 나타났으며, 특히 자료 사이의 관계를 찾는 과제 에서 그 차이가 크게 나타났다. 또한, 학생들은 꺾은선그래프와 꺾은선그래프의 혼 합보다는 꺾은선그래프와 막대그래프의 혼합을, y축이 1개인 경우보다는 2개인 경 우를, 축의 값이 모두 실수인 경우보다는 비율이 포함된 경우를 더 어려워하였다. 주요 오류 유형으로는 그래프와 y축의 혼동, 문제 이해의 부족 등이 확인되었다. 연구 결과를 토대로 초등학교 통계 교육에 대한 시사점을 제안하였다.

This study aims to analyze the comprehension of combination graphs among elementary school students in grades 4, 5, and 6 who have studied bar and line graphs in the mathematics curriculum. For this purpose, a test was designed to assess the understanding of both single and combination graphs, and it was administered to a total of 226 students. The analysis revealed that across all grades, the accuracy rates for combination graphs were lower compared to single graphs, particularly in tasks involving identifying relationships between data. Furthermore, students found it more challenging to deal with combinations of line and bar graphs than with combinations of line graphs only, with two axes rather than one, and with ratios included on the axes rather than all real numbers. The main types of errors identified included confusion between graphs and axes, subjective judgments, and a lack of understanding of the problem. Based on these findings, implications for elementary school statistics education are proposed.

Breadth-first search (BFS) is one of the most used graph kernels, and substantially affects the overall performance when processing various graphs. Since graph data are frequently used in real life for example road networks in navigation systems, high performance graph processing becomes more critical. In this study, we aim to process BFS algorithm efficiently on road network data. We propose BARON, a BFS algorithm that copes with road networks. To accelerate graph traversal, BARON reduce the occurrence of branch and memory divergences by exploiting warp-cooperative work sharing and atomic operations. With this design approach, BARON outperforms the other BFS kernels of state-of-the-art graph processing frameworks executed stably on the latest GPU architectures. For various graphs, BARON yields speedups of up to 2.88 and 5.43 over Gunrock and CuSha, respectively.

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.

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.

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/).