Assessment instrument of graph representations on sound wave topic: Development and measurement implementation by Pramudya Wahyu Pradana, Supahar

“…Therefore, it is necessary to optimize the learning process to improve students’ graph representation by providing a valid and reliable graph representation test instrument. …”
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Causality-aware graph neural networks for functional stratification and phenotype prediction at scale by Charalampos P. Triantafyllidis, Ricardo Aguas

“…We then tailor GNNs to classify each network as a single data point at graph-level, using various node embeddings and edge attributes. …”
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Insulator Surface Defect Detection Method Based on Graph Feature Diffusion Distillation by Shucai Li, Na Zhang, Gang Yang, Yannong Hou, Xingzhong Zhang

“…Aiming at the difficulties of scarcity of defect samples on the surface of power insulators, irregular morphology and insufficient pixel-level localization accuracy, this paper proposes a defect detection method based on graph feature diffusion distillation named GFDD. …”
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Semantics-Assisted Training Graph Convolution Network for Skeleton-Based Action Recognition by Huangshui Hu, Yu Cao, Yue Fang, Zhiqiang Meng

“…The skeleton-based action recognition networks often focus on extracting features such as joints from samples, while neglecting the semantic relationships inherent in actions, which also contain valuable information. …”
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Phased genome assemblies and pangenome graphs of human populations of Japan and Saudi Arabia by Maxat Kulmanov, Saeideh Ashouri, Yang Liu, Marwa Abdelhakim, Ebtehal Alsolme, Masao Nagasaki, Yasuyuki Ohkawa, Yutaka Suzuki, Rund Tawfiq, Katsushi Tokunaga, Toshiaki Katayama, Malak S. Abedalthagafi, Robert Hoehndorf, Yosuke Kawai

“…Quality evaluation of the pangenome graph by variant calling showed that our pangenome outperformed earlier linear reference genomes (GRCh38 and T2T-CHM13) and showed comparable performance to the pangenome graph provided by the Human Pangenome Reference Consortium (HPRC), with more variants found in Japanese and Saudi samples using their population-specific pangenomes. …”
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Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation by Xuan Li, Dejie Cheng, Luheng Zhang, Chengfang Zhang, Ziliang Feng

“…Graph anomaly detection is crucial in many high-impact applications across diverse fields. …”
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Knowledge Graph Representation Learning Model Based on Capsule Network and Information Fusion by Chu Zhao, Gilja So, Rui Chen

“…Based on anchor node and neighbor node and the relational sampling strategy, each node on the knowledge graph is represented by the predicted operator graph. …”
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Position-Aware Graph Neural Network for Few-Shot SAR Target Classification by Jia Zheng, Ming Li, Peng Zhang, Yan Wu, Hongmeng Chen

“…Synthetic aperture radar (SAR) target classification methods based on convolutional neural networks (CNNs) are susceptible to overfitting due to limited samples. In addition, the position and deformation variations of SAR targets can also affect the feature extraction capabilities of CNN. …”
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Multiscale Graph Transformer Network With Dynamic Superpixel Pyramid for Hyperspectral Image Classification by Tingting Wang, Yao Sun, Yunfeng Hu

“…To address these limitations, we propose a multi-scale graph transformer network (MSGTN), which captures spatial features at different scales through multiscale graph convolutional networks (GCNs) with adaptive graph structures. …”
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Graph neural networks and transfer entropy enhance forecasting of mesozooplankton community dynamics by Minhyuk Jeung, Min-Chul Jang, Kyoungsoon Shin, Seung Won Jung, Sang-Soo Baek

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Sampling nodes and hyperedges via random walks on large hypergraphs by Kazuki Nakajima, Masanao Kodakari, Masaki Aida

“…In this study, we investigate methods for simultaneously sampling nodes and hyperedges via random walks on large hypergraphs. …”
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Bayesian Inference for Drug Discovery by High Negative Samples and Oversampling by Manh Hung Le, Nam Anh Dao, Xuan Tho Dang

“…Constructing high-quality negative samples is crucial to mitigate the detrimental effects of noisy negative data and enhance model performance. …”
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Two-Stream Proximity Graph Transformer for Skeletal Person-Person Interaction Recognition With Statistical Information by Meng Li, Yaqi Wu, Qiumei Sun, Weifeng Yang

“…Specifically, we first design three types of proximity graphs based on skeletal data to encode the dynamic proximity relationship between interacting people, including frame-based, sample-based and type-based proximity graphs. …”
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Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network by Haohua Zheng, Jianchen Zhang, Heying Li, Guangxia Wang, Jianzhong Guo, Jiayao Wang

“…Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. …”
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Land-sea Clutter Classification Method Based on Multi-channel Graph Convolutional Networks by Can LI, Zengfu WANG, Xiaoxuan ZHANG, Quan PAN

“…To address these challenges, this study analyzes the correlation between adjacent azimuth-range cells, and converts land-sea clutter data from Euclidean space into graph data in non-Euclidean space, thereby incorporating sample relationships. …”
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Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion by SUN Yuanshuai, KONG Fanqin, NIE Xiaoyin, XIE Gang

“…Then, the feature structure relationship of the sample was mined through the graph generation layer to construct an instance graph. …”
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Unsupervised Contrastive Graph Kolmogorov–Arnold Networks Enhanced Cross-Modal Retrieval Hashing by Hongyu Lin, Shaofeng Shen, Yuchen Zhang, Renwei Xia

“…We propose Unsupervised Contrastive Graph Kolmogorov–Arnold Networks (GraphKAN) Enhanced Cross-modal Retrieval Hashing (UCGKANH), integrating GraphKAN with contrastive learning and hypergraph-based enhancement. …”
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Construction of Event Graph for Ship Collision Accident Analysis to Improve Maritime Traffic Safety by Jun Ma, Yang Wang, Liguang Wang, Luhui Xu, Jiong Zhao

“…At present, there are three main methods for analyzing the causes of ship collision accidents: statistical analysis, accident causation models, and knowledge graphs. With the deepening of research, the analysis methods pay more attention to the objective correlation between various factors of the accident, and the analysis results obtained are more objective and accurate. …”
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Multi-Label Feature Selection with Feature–Label Subgraph Association and Graph Representation Learning by Jinghou Ruan, Mingwei Wang, Deqing Liu, Maolin Chen, Xianjun Gao

“…In multi-label data, a sample is associated with multiple labels at the same time, and the computational complexity is manifested in the high-dimensional feature space as well as the interdependence and unbalanced distribution of labels, which leads to challenges regarding feature selection. …”
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STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning by Yi Lai, Ye Zhu, Li Li, Qing Lan, Yizheng Zuo

“…It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. …”
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