Multidimensional Electrical Networks and their Application to Exponential Speedups for Graph Problems by Jianqiang Li, Sebastian Zur

“…The exponential quantum advantage is obtained by efficiently generating quantum alternative electrical flow states and then sampling from them to find an s-t path in the welded tree circuit graph. …”
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Encrypted traffic classification encoder based on lightweight graph representation by ZhenWei Chen, XiaoXu Wei, YongSheng Wang

“…We propose using GraphSAGE with sampling averaging to encode each byte-level traffic graph into an overall representation vector for each packet. …”
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Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion by Fan Li, Yunfeng Li, Dongfeng Wang

“…To address the challenge of incomplete fault feature extraction in rolling bearing fault diagnosis under small-sample conditions, this paper proposes a Temporal-Graph Convolutional Fusion Network (T-GCFN). …”
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Adversarial Graph Regularized Deep Nonnegative Matrix Factorization for Data Representation by Songtao Li, Weigang Li, Yang Li

“…Moreover, the model gives an adversarial graph regularization representation for the manifoldization of data structures to push away the spatial distances between different sample clusters. …”
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Laplace Graph Embedding Class Specific Dictionary Learning for Face Recognition by Li Wang, Yan-Jiang Wang, Bao-Di Liu

“…In this paper, we propose the Laplace graph embedding class specific dictionary learning (LGECSDL) algorithm, which trains a weight matrix and embeds a Laplace graph to reconstruct the dictionary. …”
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Embedding-Enhanced Graph Attention Networks for Imbalanced Industrial Fault Diagnosis by Chi Zhang, Yuliang Li, Lifeng Fan, Lixiang Shen, Ting Qin

“…Intricate nonlinear interactions among measurements and imbalanced distribution of fault samples pose considerable challenges for accurate fault diagnosis in modern industrial processes. …”
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MASP: Scalable Graph-Based Planning Towards Multi-UAV Navigation by Xinyi Yang, Xinting Yang, Chao Yu, Jiayu Chen, Wenbo Ding, Huazhong Yang, Yu Wang

“…However, RL struggles with low sample efficiency when directly exploring (nearly) optimal policies in a large exploration space, especially with an increased number of drones (e.g., 10+ drones) or in complex environments (e.g., a 3D quadrotor simulator). …”
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Anomalous Node Detection in Blockchain Networks Based on Graph Neural Networks by Ze Chang, Yunfei Cai, Xiao Fan Liu, Zhenping Xie, Yuan Liu, Qianyi Zhan

“…The blockchain transaction network formed during user transactions can be represented as a graph consisting of nodes and edges, making it suitable for a graph data structure. …”
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Diverse representation-guided graph learning for multi-view metric clustering by Xiaoshuang Sang, Yang Zou, Feng Li, Ranran He

“…Additionally, many methods exploit Euclidean distance as a similarity metric, which may inaccurately measure linear relationships between samples. To tackle these challenges, we develop a novel diverse representation-guided graph learning for multi-view metric clustering (DRGMMC). …”
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Feature Coding and Graph via Transformer: Different Granularities Classification for Aircraft by Jianghao Rao, Senlin Qin, Zongyan An, Jianlin Zhang, Qiliang Bao, Zhenming Peng

“…Not only did the approach we proposed demonstrate adaptability to aircraft at different classification granularities, but it also revealed the mechanisms and characteristics of feature encoding under different sample space partitions for classification. The relationship between the oriented representation of aircraft features and various classification granularities, which is manifested through different classification criteria, shows that feature coding and graph construction via the transformer opens a new door for specific defined classification tasks where objects are divided under various partition criteria, and provides another perspective on calculation and feature extraction in fine-grained classification.…”
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Image Segmentation Method Combining Nonlocal Criteria With Graph Cut Theory by Guilin Yao, Heyuan Liu, Dongliang Zhang

“…Following this, nonlocal criteria are utilized to compute the data term within the graph cut model, which is then incorporated into the traditional graph cut framework for final segmentation. …”
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Local Differential Privacy Graph Data Modeling Method for Link Prediction by HANQilong, WUXiaoming

“…At the same time , combined with the subgraph partitioning strategy of two rounds of data collection , the subgraph cluster feature of the original graph is retained. Finally , a personalized sampling randomized response local differential privacy ( PSRR-LDP) graph data perturbing algorithm was implemented , and the PSRR-LDP algorithm is theoretically proved to satisfy the ε -edge Local differential privacy. …”
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Distributed variational sparse Bayesian compressed sensing based on factor graphs by Cui-tao ZHU, Fan YANG, Han-xin WANG, Zhong-jie LI

“…A distributed variational sparse Bayesian compressed spectrum sensing algorithm based on factor graph was proposed,which decomposed the global spectrum sensing problem into local problem based on factor and variation.Belief propagation was used for the statistical inference of the spectrum occupancy,to implement the “soft fusion”.The temporal and spatial correlation information providing two-dimensional redundancies was exchanged among cooperative cognitive users to improve the detection performance under low SNR.Meanwhile,the algorithm prunes the divergence of hyper-parameters and the corresponding basis functions for reducing the load of communication.The simulation results show that this method can effectively achieve performance of spectrum sensing under a low sampling rate and the low SNR.…”
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Transient Stability Assessment of Graph Attention Networks Considering Data Missing by Shengcun ZHOU, Yi LUO, Xuancheng YI, Yaning WU, Ding LI, Yi XIONG

“…Finally, the focus loss function is used to enhance the model's learning ability for unstable samples. The simulation results show that the proposed method can maximize the use of observable data with high precision and strong robustness, and is not limited by the network topology and easy to migrate.…”
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Early detection of cognitive decline with deep learning and graph-based modeling by Sunita Patil, Swetta Kukreja

“…This work introduces a Multimodal Fusion Cognitive Assessment Framework that leverages advanced deep learning and graph intelligence to enhance early identification accuracy. …”
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Aircraft Sensor Fault Diagnosis Based on GraphSage and Attention Mechanism by Zhongzhi Li, Jinyi Ma, Rong Fan, Yunmei Zhao, Jianliang Ai, Yiqun Dong

“…Next, node and neighbor information is aggregated through graph sampling and attention-based aggregation methods, strengthening the extraction of fault features. …”
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Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology by Olga Fourkioti, Matt De Vries, Reed Naidoo, Chris Bakal

“…To address this, many graph-based methods have been proposed, where each WSI is represented as a graph with tiles as nodes and edges defined by specific spatial relationships. …”
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ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform by Bo Zhao, Hucheng Zhou, Guoqiang Li, Yihua Huang

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A complexity transition in displaced Gaussian Boson sampling by Zhenghao Li, Naomi R. Solomons, Jacob F. F. Bulmer, Raj B. Patel, Ian A. Walmsley

“…Abstract Gaussian Boson Sampling (GBS) is the problem of sampling from the output of photon-number-resolving measurements of squeezed states input to a linear optical interferometer. …”
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Spatial Variables and Land Use Change Models: A Study on Conditioning Patterns of Natural Vegetation Suppression and Persistence by Macleidi Varnier, Eliseu José Weber

“…In modeling software, this step is usually performed by characterizing samples based on spatial variables. Despite the importance of this stage, the evaluation of the change and persistence patterns is often neglected by the scientific community. …”
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