Multi-label feature selection based on dynamic graph Laplacian by Yonghao LI, Liang HU, Ping ZHANG, Wanfu GAO

“…In view of the problems that graph-based multi-label feature selection methods ignore the dynamic change of graph Laplacian matrix, as well as such methods employ logical-value labels to guide feature selection process and loses label information, a multi-label feature selection method based on both dynamic graph Laplacian matrix and real-value labels was proposed.The robust low-dimensional space of feature matrix was used to construct a dynamic graph Laplacian matrix, and the robust low-dimensional space was used as the real-value label space.Furthermore, manifold and non-negative constraints were adopted to transform logical labels into real-valued labels to address the issues mentioned above.The proposed method was compared to three multi-label feature selection methods on nine multi-label benchmark data sets in experiments.The experimental results demonstrate that the proposed multi-label feature selection method can obtain the higher quality feature subset and achieve good classification performance.…”
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Analyzing cryptographic algorithm efficiency with in graph-based encryption models by Yashmin Banu, Biplab Kumar Rath, Debasis Gountia

“…This study evaluates RSA and ElGamal encryption on text, image, audio, and data files of varying sizes using graph-based models. …”
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Text-Enhanced Graph Attention Hashing for Cross-Modal Retrieval by Qiang Zou, Shuli Cheng, Anyu Du, Jiayi Chen

“…Deep hashing technology, known for its low-cost storage and rapid retrieval, has become a focal point in cross-modal retrieval research as multimodal data continue to grow. …”
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A Graph Representation Learning-Based Method for Event Prediction by Xi Zeng, Guangchun Luo, Ke Qin, Pengyi Zheng

“…By converting the career graph of employees into low-dimensional representations, the effectiveness of the dynamic graph representation learning method in predicting employee career decisions is validated. …”
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Intelligent Flood Dispatching in River Basins Based on Knowledge Graphs by SONG Wanli, XIONG Xian, YANG Haihua, ZHU Sha, FENG Yuwei, WANG Zhaoli

“…To this end, this paper introduces the basic theory and technical methods of knowledge graphs. Meanwhile, by taking the cascade hydropower station in the Beipanjiang River Basin as an example, it analyzes the data such as basic performance parameters and dispatching procedures of the reservoir. …”
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Large Language Models for Knowledge Graph Embedding: A Survey by Bingchen Liu, Yuanyuan Fang, Naixing Xu, Shihao Hou, Xin Li, Qian Li

“…Traditional KGE representation learning methods map entities and relations into a low-dimensional vector space, enabling the triples in the knowledge graph to satisfy a specific scoring function in the vector space. …”
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KITE-DDI: A Knowledge Graph Integrated Transformer Model for Accurately Predicting Drug-Drug Interaction Events From Drug SMILES and Biomedical Knowledge Graph by Azwad Tamir, Jiann-Shiun Yuan

“…The model does not depend on heuristic models for generating embeddings and has a minimal number of hyperparameters, making it easy to use while demonstrating outstanding performance in low-data scenarios.…”
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Multimodal depression detection based on an attention graph convolution and transformer by Xiaowen Jia, Jingxia Chen, Kexin Liu, Qian Wang, Jialing He

“…Traditional depression detection methods typically rely on single-modal data, but these approaches are limited by individual differences, noise interference, and emotional fluctuations. …”
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RQPool: A Novel Multi-Branch Graph-Level Anomaly Detection by Aaron Alex Philip, Ziad Kobti

“…By analyzing these structures, we can uncover anomalies that are not apparent using traditional methods. However, current Graph-based AD techniques face significant challenges, particularly low accuracy on larger datasets. …”
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Weapon equipment question answering system based on BERT and knowledge graph by WANG Bo, JIANG Xuping, HUANG Qihong

“…To address issues such as data redundancy, high interaction difficulty, and low match accuracy of question answers, this paper constructs a Q&A system based on a knowledge graph for weaponry and equipment. …”
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Robust self supervised symmetric nonnegative matrix factorization to the graph clustering by Yi Ru, Michael Gruninger, YangLiu Dou

“…Traditional Nonnegative Matrix Factorization (NMF) methods have shown promise in clustering tasks by providing low-dimensional representations of data. However, most existing NMF-based approaches are highly sensitive to noise and outliers, leading to suboptimal performance in real-world scenarios. …”
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DrugFormer: Graph‐Enhanced Language Model to Predict Drug Sensitivity by Xiaona Liu, Qing Wang, Minghao Zhou, Yanfei Wang, Xuefeng Wang, Xiaobo Zhou, Qianqian Song

“…DrugFormer integrates both serialized gene tokens and gene‐based knowledge graphs for the accurate predictions of drug response. …”
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An edge server placement based on graph clustering in mobile edge computing by Shanshan Zhang, Jiong Yu, Mingjian Hu

“…Abstract With the exponential growth of mobile devices and data traffic, mobile edge computing has become a promising technology, and the placement of edge servers plays a key role in providing efficient and low-latency services. …”
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Hierarchical GraphCut Phase Unwrapping Based on Invariance of Diffeomorphisms Framework by Xiang Gao, Xinmu Wang, Zhou Zhao, Junqi Huang, Xianfeng David Gu

“…An odd number of diffeomorphisms are precomputed from the input phase data, and a hierarchical GraphCut algorithm is applied in each corresponding domain. …”
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Level of overweight and obesity surpassed underweight among women in 40 low and middle-income countries: Findings from a multilevel multinomial analysis of population survey data. by Kusse Urmale Mare, Kebede Gemeda Sabo, Beriso Furo Wengoro, Begetayinoral Kussia Lahole

“…Thus, this study intended to provide insights into the current level of malnutrition among women of reproductive age in low- and middle-income countries.<h4>Methods</h4>A secondary analysis of Demographic and Health Survey data from 40 low- and middle-income countries was performed using a weighted sample of 1,044,340 women of reproductive age. …”
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BIM-GIS Integration for Interactive, Open and Low-Cost 3D Land Use Registration and Urban Neighbourhood Management by Dimitra Andritsou, Chryssy Potsiou

“…The methodology includes: 1) the creation of approximate BIMs by utilizing open and available data and platforms, 2) modelling of land uses as 3D volumetric prisms, 3) registering semantic and geometrical land use information and 4) crafting statistical graphs, emphasising on data compilation and BIM-GIS standards implementation.…”
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Voxel-Wise&#x2009;Brain&#x2009;Graphs&#x2009;From&#x2009;Diffusion&#x2009;MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI by Hamid Behjat, Anjali Tarun, David Abramian, Martin Larsson, Dimitri Van De Ville

“…By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show that brain activity can be well approximated by a small subset of low-frequency components. …”
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From Pixels to Insights: Unsupervised Knowledge Graph Generation with Large Language Model by Lei Chen, Zhenyu Chen, Wei Yang, Shi Liu, Yong Li

“…We then propose an iterative fine-tuning process that uses this self-supervised information, enabling the fine-tuned LLM to recognize the triplets needed to construct the knowledge graph. To improve the accuracy of triplet extraction, we introduce filtering strategies that effectively remove low-confidence training data. …”
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Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization by Miguel García-Ortegón, Srijit Seal, Carl Rasmussen, Andreas Bender, Sergio Bacallado

“…Overall, our results suggest that NPs on molecular graphs hold great potential for molecular property prediction in the low-data setting. …”
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Multi-fidelity graph neural networks for predicting toluene/water partition coefficients by Thomas Nevolianis, Jan G. Rittig, Alexander Mitsos, Kai Leonhard

“…To address the limitation of available data, we apply multi-fidelity learning approaches leveraging a quantum chemical dataset (low fidelity) of approximately 9000 entries generated by COSMO-RS and an experimental dataset (high fidelity) of about 250 entries collected from the literature. …”
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