Bio inspired feature selection and graph learning for sepsis risk stratification by D. Siri, Raviteja Kocherla, Sudharshan Tumkunta, Pamula Udayaraju, Krishna Chaitanya Gogineni, Gowtham Mamidisetti, Nanditha Boddu

Subjects: “…Generative pre-training-graph neural networks…”
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A Mechanical Fault Identification Method for On-Load Tap Changers Based on Hybrid Time—Frequency Graphs of Vibration Signals and DSCNN-SVM with Small Sample Sizes by Yanhui Shi, Yanjun Ruan, Liangchuang Li, Bo Zhang, Yichao Huang, Mao Xia, Kaiwen Yuan, Zhao Luo, Sizhao Lu

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Application research of sample data generation based on improved Cycle-GAN in intrusion detection by ZENG Qingpeng, GUO Hangkai

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A Novel Assembly Process Knowledge Graph Inference Method Integrating Logical Rules and Embedded Learning by Peilin Shao, Zhicheng Huang, Yongqiang Wan, Lihong Qiao, Xinzheng Xu, Chao Chen, Zhujia Li, Nabil Anwer, Yifan Qie

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Multi-behavior aware recommendation with joint contrastive learning and reinforced negative sampling by Yujia Du, Zhengtao Yu, Hongbin Wang

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MCGS-ReID: A Visible-Infrared Vehicle Reidentification Method Using Modal-Cross Graph Sampler by Jianfei Liu, Chunhui Zhao, Chen Zhao, Nan Su, Wanxuan Lu, Yiming Yan, Shou Feng, Yunfei Qu

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Experience sampling method studies in physical activity research: the relevance of causal reasoning by Louise Poppe, Annick L. De Paepe, Benedicte Deforche, Delfien Van Dyck, Tom Loeys, Jelle Van Cauwenberg

Subjects: “…Experience sampling method…”
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Examining Predictors of Environmental Health Knowledge and Competences in a Sample of Catalan Paediatricians by Mikel Subiza‐Pérez, Ferran Campillo i López, Kalliopi Vrostsou, Jesús Ibarluzea

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Graph of graphs analysis for multiplexed data with application to imaging mass cytometry. by Ya-Wei Eileen Lin, Tal Shnitzer, Ronen Talmon, Franz Villarroel-Espindola, Shruti Desai, Kurt Schalper, Yuval Kluger

“…Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. …”
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Transductive zero-shot learning via knowledge graph and graph convolutional networks by Qiong Li, Xin Sun, Junyu Dong

“…To tackle this problem, we propose a transductive zero-shot learning method, based on Knowledge Graph and Graph Convolutional Network. We firstly learn a knowledge graph, where each node represents a category encoded by its semantic embedding. …”
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An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data by Huan Tao, Ziyang Li, Shengdong Nie, Hengkai Li, Dan Zhao

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GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling by Chaoyi Li, Hongxin Xiang, Wenjie Du, Tengfei Ma, Haowen Chen, Xiangxiang Zeng, Lei Xu

“…To alleviate the above challenge, we propose a novel molecular graph contrastive learning method via geometry image modeling, called GraphGIM, which enhances the diversity between sample pairs. …”
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Intrusion Detection in IoT Networks Using Dynamic Graph Modeling and Graph-Based Neural Networks by William Villegas-Ch, Jaime Govea, Alexandra Maldonado Navarro, Pablo Palacios Jativa

“…The model also proved scalable, maintaining an inference time of 2.5 ms per sample on graphs of up to 10,000 nodes, making it viable for real-time deployment. …”
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Research on GNNs with stable learning by Wenbin Li, Wenxuan Wei, Peiyang Wang, Li Pan, Bo Yang, Yanling Xu

Subjects: “…Graph neural networks…”
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Inferring building functions from a weighted graph isomorphic network based on the building-POI graph by Zhang Ya, Jiping Liu, Wang Yong, Luo An, Shenghua Xu, Zhiran Zhang

“…However, prevailing studies utilizing graph neural networks (GNNs) often regard building function inference as a node classification task, which fails to solve the problem of model performance bias toward residential buildings caused by sample imbalance. …”
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A New AB Initio Repeats Finding Algorithm for Reference Genome by Shuaibin Lian, Ke Gong, Xiangli Zhang, Xinwu Chen

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Graph-Based Semi-Supervised Learning with Bipartite Graph for Large-Scale Data and Prediction of Unseen Data by Mohammad Alemi, Alireza Bosaghzadeh, Fadi Dornaika

“…Firstly, many studies treat all samples equally in terms of weight and influence, disregarding the potential increased importance of samples near decision boundaries. …”
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Applicability of elite samples in solving the traveling salesman problem by Goldberg model by Valery G. Kobak, Irma S. Rudova

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Efficient identification of maximum independent sets in stochastic multilayer graphs with learning automata by Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi, Alireza Rezvanian

“…In this paper, we introduce the stochastic version of the maximum independent set and propose five algorithms based on learning automata to identify maximum independent sets in the stochastic multilayer graphs. Our approach utilizes learning automata to provide a guided sampling from candidate independent sets of the stochastic multilayer graph, aiming to identify the independent set with the maximum expected value while utilizing fewer vertex samples than standard methods that do not incorporate the learning. …”
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PGCF: Perception graph collaborative filtering for recommendation by Caihong Mu, Keyang Zhang, Jiashen Luo, Yi Liu

“…For the loss function, we design a margin-perception Bayesian personalized ranking (MBPR) loss function, which introduces a self-perception margin, requiring the predicted score of the user-positive sample to be greater than that of the user-negative sample, and also greater than the sum of the predicted score of the user-negative sample and the margin. …”
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