Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images by Jingyi CAO, Yang ZHANG, Ya’nan YOU, Yamin WANG, Feng YANG, Weijia REN, Jun LIU

“…Guided by multilevel essential feature modeling theory, we propose a SAR image target recognition method based on graph networks and invariant feature perception. …”
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A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

“…The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and spatial–spectral features to improve its representation ability. Firstly, a Graph Convolutional Network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. …”
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The effect of a number of quantitative traits on seed productivity of alfalfa samples by К. N. Goryunov

“…The seed productivity of the collection samples varied from 27.2 g/m2 (the sample ‘SGP-128') to 101.0 g/m2 (the sample ‘SGP-414'). …”
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Distributed K-Means algorithm based on a Spark optimization sample. by Yongan Feng, Jiapeng Zou, Wanjun Liu, Fu Lv

“…Additionally, SOSK-Means incorporates a Directed Acyclic Graph (DAG) to optimize performance through distributed strategies, leveraging the capabilities of the Spark framework. …”
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Goistrat: gene-of-interest-based sample stratification for the evaluation of functional differences by Carlos Uziel Pérez Malla, Jessica Kalla, Andreas Tiefenbacher, Gabriel Wasinger, Kilian Kluge, Gerda Egger, Raheleh Sheibani-Tezerji

“…Results To address this gap, we present a novel workflow for the stratification and further analysis of multi-omics samples with matched RNA-Seq data that relies on MSigDB curated gene sets, graph machine learning and ensemble clustering. …”
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VALIDATION METHOD FOR DETERMINATION OF THE SULFUR HEXAFLUORIDE IN PLASMA SAMPLES OF EXPERIMENTAL ANIMALS by S. S. Vlasov, S. V. Krivoshchekov, M. K. Zamanova, M. V. Belousov, A. M. Guriev, M. S. Yusubov

“…Samples were stored for 24 hours to determine the short-term stability. …”
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Meta-GCANet: Meta-Graph Coupled Aware Network for Cross-Modality Person Re-Identification by Jiale Zhang, Baohua Zhang, Dongyang Wu

“…These samples are then stacked and interact with modality-shared discriminative feature nodes to construct a coupled node graph. …”
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Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent by Korfmann, Kevin, Sellinger, Thibaut Paul Patrick, Freund, Fabian, Fumagalli, Matteo, Tellier, Aurélien

“…The current methods developed to detect such multiple merger events do not account for complex demographic scenarios or recombination, and require large sample sizes. We tackle these limitations by developing two novel and different approaches to infer multiple merger events from sequence data or the ancestral recombination graph (ARG): a sequentially Markovian coalescent (SMβC) and a graph neural network (GNNcoal). …”
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Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks by Stavros Orfanoudakis, Valentin Robu, E. Mauricio Salazar, Peter Palensky, Pedro P. Vergara

“…We prove that EV-GNN enhances classic Reinforcement Learning (RL) algorithms’ scalability and sample efficiency by combining an end-to-end Graph Neural Network (GNN) architecture with RL and employing a branch pruning technique. …”
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Using graph machine learning to identify functioning in patients with low back pain in terms of ICF by Linda Nieminen, Harri Ketamo, Jari Vuori, Markku Kankaanpää

“…Headai Graphmind was then tested for its ability to match free text with ICF codes on a sample of 20 patients. The results were compared against the findings of a domain expert. …”
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Determining the optimal generalization operators for building footprints using an improved graph neural network model by Xinyu Niu, Haizhong Qian, Xiao Wang, Limin Xie, Longfei Cui

“…SNGNN has been experimentally validated using sample datasets for Ningbo, China, at 1:10,000 and 1:25,000. …”
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DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes by Wei Zhang, Zeqi Xu, Ruochen Yu, Mingfeng Jiang, Qi Dai

“…Topological features, which are associated with gene graphs and cell graphs, were disregarded in previous works. …”
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PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences by Amin Khodaei, Sania Eskandari, Hadi Sharifi, Behzad Mozaffari-Tazehkand

“…On the other hand, it has been demonstrated that the use of graph algorithms and machine learning in the analysis and examination of virus samples and even viral variants can yield beneficial results. …”
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Auxiliary Task Graph Convolution Network: A Skeleton-Based Action Recognition for Practical Use by Junsu Cho, Seungwon Kim, Chi-Min Oh, Jeong-Min Park

“…In this study, we propose an Auxiliary Task Graph Convolution Network (AT-GCN) with low and high-frame pathways while supporting a new sampling method. …”
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CLB-Defense: based on contrastive learning defense for graph neural network against backdoor attack by Jinyin CHEN, Haiyang XIONG, Haonan MA, Yayu ZHENG

“…For the problem that the existing backdoor attack defense methods are difficult to deal with irregular and unstructured discrete graph data to alleviate the threat of backdoor attacks, a backdoor attack defense method for GNN based on contrastive learning was proposed, namely CLB-Defense.Specifically, a contrastive model was built by using contrastive learning in an unsupervised way, which searches suspicious backdoored samples.Then the suspicious backdoored samples were reshaped by using the graph importance indexes and the label smoothing strategy, and the defense against graph backdoor attack was realized.Finally, extensive experimental results show that CLB-Defense realizes the effect of defense performance on four public datasets and five popular graph backdoor attacks, e.g., CLB-Defense can reduce the attack success rate by an average of 75.66% (compared with the baselines, an improvement of 54.01%).…”
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Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma by Suryadipto Sarkar, Anna Möller, Anne Hartebrodt, Michael Erdmann, Christian Ostalecki, Andreas Baur, David B. Blumenthal

“…To characterize CTCL in comparison to these differential diagnoses, we carried out multi-antigen imaging on 69 skin tissue samples (21 CTCL, 23 AD, 25 PSO). The resulting protein abundance maps were then analyzed via scoring functions to quantify the heterogeneity of the individual cells’ neighborhoods within spatial graphs inferred from the cells’ positions in the tissue samples. …”
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Scene Graph and Natural Language-Based Semantic Image Retrieval Using Vision Sensor Data by Jaehoon Kim, Byoung Chul Ko

“…In addition, we incorporate a hard negative mining strategy, allowing the model to effectively learn from more challenging negative samples. The experimental results on the Visual Genome dataset show that the proposed method achieves a top nDCG@50 score of 0.745, improving retrieval performance by approximately 7.7 percentage points compared to random sampling with full graphs. …”
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Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis by Pradeep K. Yadalam, Prabhu Manickam Natarajan, Carlos M. Ardila

“…VGAE, a deep learning model, captures complex graph relationships for tasks like link prediction and edge detection. …”
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GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection by Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki

“…An efficient Generative Adversarial Network (GAN) is employed to generate synthetic malware samples, effectively augmenting the dataset and enhancing the diversity of the malware samples under study process. …”
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Constructing Mathematical Models to Find a Relationship between Physical Compounds Using the Graph Theory by Ghassan E. Arif1, Samer R. Yassen, Abdulsameeh.F.Abdul Aziz, Summar.W.Omer

“… The current study aims to construct a mathematical models in order to assist the researchers to determine the specific activities of radionuclides in soil samples where we found a relationship between uranium element and radionuclide activities. …”
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