Optimization complexity and resource minimization of emitter-based photonic graph state generation protocols by Evangelia Takou, Edwin Barnes, Sophia E. Economou

“…Abstract Photonic graph states are important for measurement- and fusion-based quantum computing, quantum networks, and sensing. …”
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BioGAN: Enhancing Transcriptomic Data Generation with Biological Knowledge by Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli

“…In this work, we present BioGAN, a novel generative framework that, for the first time, incorporates graph neural networks into a generative adversarial network architecture for transcriptomic data generation. …”
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Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning by Vijay Govindarajan, Junaid Hussain Muzamal

“…Network flows are modeled as graphs to capture relational patterns among IP addresses and services, and a Graph Neural Network (GNN) is used to extract structured embeddings. …”
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MONSTROUS: a web-based chemical-transporter interaction profiler by Mohamed Diwan M. AbdulHameed, Mohamed Diwan M. AbdulHameed, Souvik Dey, Souvik Dey, Zhen Xu, Zhen Xu, Ben Clancy, Ben Clancy, Valmik Desai, Valmik Desai, Anders Wallqvist

“…We utilized publicly available data and developed machine learning or similarity-based classification models to predict inhibitors and substrates for 12 transporters. We used graph convolutional neural networks (GCNNs) to develop predictive models for transporters with sufficient bioactivity data, and we implemented two-dimensional similarity-based approach for those without sufficient data. …”
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Vision-Based Fall Risk Assessment Through Attention Augmented Neural Encoding and Data Augmentation by Chunhua Pan, Rui Miao, Qing Zhang, Boting Qu, Xin Wang

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A GNN-Based QSPR Model for Surfactant Properties by Seokgyun Ham, Xin Wang, Hongwei Zhang, Brian Lattimer, Rui Qiao

“…However, the relationship between surfactant structure and these properties is complex and difficult to predict theoretically. Here, we develop a graph neural network (GNN)-based quantitative structure–property relationship (QSPR) model to predict the CMC, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>γ</mi></mrow><mrow><mi>c</mi><mi>m</mi><mi>c</mi></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>Γ</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula>. …”
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A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems by Md Shahab Uddin, Ahsan Ahmed, Md Aktarujjaman, Mohammad Moniruzzaman, Mumtahina Ahmed, M. F. Mridha, Md. Jakir Hossen

“…This paper proposes a novel hybrid framework that integrates reinforcement learning (RL) with knowledge graph-augmented neural networks to optimize billing decisions while preserving diagnostic accuracy. …”
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Malicious software identification based on deep learning algorithms and API feature extraction by Wei Sun

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TraitBertGCN: Personality Trait Prediction Using BertGCN with Data Fusion Technique by Muhammad Waqas, Fengli Zhang, Asif Ali Laghari, Ahmad Almadhor, Filip Petrinec, Asif Iqbal, Mian Muhammad Yasir Khalil

“…Initially, this work integrates a pre-trained language model, Bidirectional Encoder Representations from Transformers (BERT), with a three-layer Graph Convolutional Network (GCN) to leverage large-scale language understanding and graph-based learning for personality prediction. …”
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PolyAttractNet: Graph-Based Polygonal Segmentation of Building Footprints Using Attraction Field Maps by M. Kamran, M. Moein Sheikholeslami, G. Sohn

“…Our approach incorporates Attraction Field Maps (AFMs) within a Graph Neural Network (GNN) framework, combined with an enhanced Mask R-CNN backbone. …”
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An Unmanned Delivery Vehicle Path-Planning Method Based on Point-Graph Joint Embedding and Dual Decoders by Jiale Cheng, Zhiwei Ni, Wentao Liu, Qian Chen, Rui Yan

“…This novel deep neural network model incorporates an attention mechanism and applies a method called point-graph joint embedding and dual decoders (PGDD) to solve the problem. …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

“…To address these challenges, we propose Med-DGTN, a dynamically integrated framework designed to advance multi-label classification performance in clinical imaging analytics.MethodsThe proposed Med-DGTN (Dynamic Graph Transformer Network with Adaptive Wavelet Fusion) introduces three key innovations: (1) A cross-modal alignment mechanism integrating convolutional visual patterns with graph-based semantic dependencies through conditionally reweighted adjacency matrices; (2) Wavelet-transform-enhanced dense blocks (WTDense) employing multi-frequency decomposition to amplify low-frequency pathological biomarkers; (3) An adaptive fusion architecture optimizing multi-scale feature hierarchies across spatial and spectral domains.ResultsValidated on two public medical imaging benchmarks, Med-DGTN demonstrates superior performance across modalities: (1) Achieving a mean average precision (mAP) of 70.65% on the retinal imaging dataset (MuReD2022), surpassing previous state-of-the-art methods by 2.68 percentage points. (2) On the chest X-ray dataset (ChestXray14), Med-DGTN achieves an average Area Under the Curve (AUC) of 0.841. …”
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It Is Better to Be Semi-Regular When You Have a Low Degree by Theodore Kolokolnikov

“…More generally, we study random semi-regular graphs whose average degree is <i>d</i>, not necessarily an integer. …”
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Knowledge graph and frontier trends in melanoma-associated ncRNAs: a bibliometric analysis from 2006 to 2023 by Ru Wang, Xiao-yan Zhu, Yi Wang, Yi Wang

“…R Studio (Version 4.3.1), Scimago Graphica (Version 1.0.36), VOSviewer version (1.6.19), and Citespace (6.2.4R) were used to analyze the publications, countries, journals, institutions, authors, keywords, references, and other relevant data and to build collaboration network graphs and co-occurrence network graphs accordingly.ResultsA total of 1,222 articles were retrieved, involving 4,894 authors, 385 journals, 43,220 references, 2413 keywords, and 1,651 institutions in 47 countries. …”
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GC-MT: A Novel Vessel Trajectory Sequence Prediction Method for Marine Regions by Haixiong Ye, Wei Wang, Xiliang Zhang

“…This study introduces a Graph Convolutional Mamba Network (GC-MT) utilizing AIS data for predicting vessel trajectories. …”
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Memory-augment graph transformer based unsupervised detection model for identifying performance anomalies in highly-dynamic cloud environments by Huangyining Gao, Ruyue Xin, Peng Chen, Xi Li, Ning Lu, Peng You

“…Abstract Cloud computing systems provide highly available and scalable computing, storage, and network resources to meet various service demands. …”
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Zero-Shot Remote Sensing Scene Classification Based on Automatic Knowledge Graph and Dual-Branch Semantic Correlation Supervision by Chao Wang, Jiajun Yang, Tanvir Ahmed, Yang Zhao, Tong Zhang, Bing Sun, Tao Xie, Jie Wang, Tianyu Chen

“…However, current graphs rely heavily on expert manual interpretation, making them susceptible to human biases and difficult to expand. …”
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Hierarchy measure for complex networks. by Enys Mones, Lilla Vicsek, Tamás Vicsek

“…The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. …”
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Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding by Yelan Wu, Pugang Cao, Meng Xu, Yue Zhang, Xiaoqin Lian, Chongchong Yu

“…To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. …”
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Hybrid AI and Big Data Solutions for Dynamic Urban Planning and Smart City Optimization by Wei Zhu, Wei He, Qingsong Li

“…This study introduces a novel approach by combining Graph Neural Networks (GNNs) with Simulated Annealing (SA) to tackle these challenges in urban planning. …”
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