Laser treatment for urinary incontinence in elite female athletes analyzed using a discrete mathematics approach by Nobuo Okui

“…We employ a discrete mathematics analytical approach using network graphs to identify key factors influencing treatment outcomes and to address the challenges of small sample sizes and unknown variables in this population. …”
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Evaluating convergence between two data visualization literacy assessments by Erik Brockbank, Arnav Verma, Hannah Lloyd, Holly Huey, Lace Padilla, Judith E. Fan

“…Here, we administered two widely used graph comprehension assessments (Galesic and Garcia-Retamero in Med Dec Mak 31:444–457, 2011; Lee et al. in IEEE Trans Vis Comput Graph 235:51–560, 2016) to both a university-based convenience sample and a demographically representative sample of adult participants in the USA (N=1,113). …”
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An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features by Ziheng Wang, Miao Ye, Jin Cheng, Cheng Zhu, Yong Wang

“…Additionally, they struggle with small sample scenarios because they do not effectively map features to classes. …”
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GCN-based weakly-supervised community detection with updated structure centres selection by Liping Deng, Bing Guo, Wen Zheng

“…Semi-supervised network learning requires a certain amount of known samples, while sample annotation is time-consuming and laborious. …”
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Multi-view adversarial attack defending method for host intrusion detection by WANG Fei, QIAN Kehan, LYU Mingqi, ZHU Tiantian, CHEN Honglong

“…In recent years, to address increasingly sophisticated host attacks, provenance graphs were leveraged to parse kernel audit logs, and graph neural network (GNN) were employed to train detection models, significantly enhancing detection performance. …”
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Local density-based similarity matrix construction for spectral clustering by Jian WU, Zhi-ming CUI, Yu-jie SHI, Sheng-li SHENG, Sheng-rong GONG

“…Firstly, by analyzing distribution characteristics of sample data points, the definition of local density was given, sorting operation on sample point set from dense to sparse according to sample points'local density was did, and undirected graph in accordance with the designed connection strategy was constructed; then, on the basis of GN algorithm's thinking, a calculation method of weight matrix using edge betweenness was given, and similarity matrix of spectral clustering via data conversion was got; lastly, the class number by appearing position of the first eigengap maximum was determined, and the classification of sample point set in eigenvector space by means of classical cluster g method was realized. …”
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Local density-based similarity matrix construction for spectral clustering by Jian WU, Zhi-ming CUI, Yu-jie SHI, Sheng-li SHENG, Sheng-rong GONG

“…Firstly, by analyzing distribution characteristics of sample data points, the definition of local density was given, sorting operation on sample point set from dense to sparse according to sample points'local density was did, and undirected graph in accordance with the designed connection strategy was constructed; then, on the basis of GN algorithm's thinking, a calculation method of weight matrix using edge betweenness was given, and similarity matrix of spectral clustering via data conversion was got; lastly, the class number by appearing position of the first eigengap maximum was determined, and the classification of sample point set in eigenvector space by means of classical cluster g method was realized. …”
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Using the Space Syntax Analysis to Examine the Spatial Configuration of Houses Layouts and Its Transformation over Time in Biskra City by Rihane Barkat, Yassine Bada, Yasemin İnce Güney, Hafnaoui Hamzaoui

“…In the context of discovering the differentiation and the similarities between the samples that consist of 16 houses: four houses from each period; an analysis has been carried out by applying the “Gammaanalysis” using the A-graph program in detecting the genotypical consistencies in their patterning.  …”
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Evaluating state-based network dynamics in anhedonia by Angela Pisoni, Jeffrey Browndyke, Simon W. Davis, Moria Smoski

“…The present study addressed this gap in the literature by taking a graph theoretical approach to characterizing state-based (i.e., reward anticipation, rest) network dynamics in a transdiagnostic sample of adults with clinically significant anhedonia (n = 77). …”
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Causal Discovery for Patient Classification Using Health-Related Quality of Life Questionnaires by Maria Ganopoulou, Konstantinos Fokianos, Christos Bakirtzis, Lefteris Angelis, Theodoros Moysiadis

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Convolution of the physical point cloud for predicting the self-assembly of colloidal particles by Seunghoon Kang, Young Jin Lee, Kyung Hyun Ahn

“…The phases predicted by our model are not limited to liquid-like dispersions and solid–liquid phase separations, where thermodynamic equilibrium differs, but also include sample-spanning gel structures, where only kinetics differ while thermodynamics remain the same. …”
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Text Geolocation Prediction via Self-Supervised Learning by Yuxing Wu, Zhuang Zeng, Kaiyue Liu, Zhouzheng Xu, Yaqin Ye, Shunping Zhou, Huangbao Yao, Shengwen Li

“…As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount of labeled samples to train model parameters. To address this limitation, this paper presents a method for text geolocation prediction without labeled samples, namely GeoSG (Geographic Self-Supervised Geolocation) model, which leverages self-supervised learning to improve text geolocation prediction in situations where labeled samples are unavailable. …”
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Hybrid CNN-GCN Network for Hyperspectral Image Classification by Cuiping Shi, Diling Liao, Liguo Wang

“…In addition, a small number of training samples is also a reason for hindering high-performance HSI classification. …”
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A Comprehensive Review of Path-Planning Algorithms for Planetary Rover Exploration by Qingliang Miao, Guangfei Wei

“…Next, we examine rule-based path-planning approaches, including graph search-based methods, potential field methods, sampling-based techniques, and dynamic window approaches, analyzing representative algorithms in each category. …”
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GSIDroid: A Suspicious Subgraph-Driven and Interpretable Android Malware Detection System by Hong Huang, Weitao Huang, Feng Jiang

“…Machine learning-based detection approaches have improved the accuracy of malware identification, thereby providing more effective protection for Android users. However, graph-based detection methods rely on whole-graph computations instead of subgraph-level analyses, and they often ignore the semantic information of individual nodes. …”
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Efficient network attack path optimization method based on prior knowledge-based PPO algorithm by Qiuxiang Li, Jianping Wu

“…The algorithm first designs action filtering rules based on prior knowledge extracted from the attack graph. Based on these rules, action mask vectors are generated before each action sampling phase to modify the distribution of the output of the policy network. …”
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Impact of Network Complexity on the Computational Performance of Ising Machines by Seokmin Hong

“…Graph sparsification provides a useful means of adjusting network complexity for practical and scalable applications with moderate computational overhead. …”
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A Chinese Few-Shot Named-Entity Recognition Model Based on Multi-Label Prompts and Boundary Information by Cong Zhou, Baohua Huang, Yunjie Ling

“…Activating the relevant parameters in PLM associated with the corresponding entity labels through the prompt information improved the model’s performance in entity recognition under small-sample data. Secondly, by using a Graph Attention Network (GAT) to construct the boundary information extraction module, we integrated boundary information with text features, allowing the model to pay more attention to features near the boundaries when recognizing entities, thereby improving the accuracy of entity boundary recognition. …”
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Computational Construction of Sequential Efficient Designs for the Second-Order Model by Norah Alshammari, Stelios Georgiou, Stella Stylianou

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Identification method for malicious traffic in industrial Internet under new unknown attack scenarios by ZENG Fanyi, MAN Dapeng, XU Chen, HAN Shuai, WANG Huanran, ZHOU Xue, LI Xinchun, YANG Wu

“…Finally, the statistical independence of high-dimensional traffic features was realized by applying graph representation learning and stable learning strategies, combined with adaptive sample weighting and collaborative loss optimization methods. …”
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