Robust Non-Negative Matrix Tri-Factorization with Dual Hyper-Graph Regularization by Jiyang Yu, Hangjun Che, Man-Fai Leung, Cheng Liu, Wenhui Wu, Zheng Yan

“…Secondly, a dual hyper-graph is established to uncover the higher-order inherent information within sample space and feature spaces for clustering. …”
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ergm 4: New Features for Analyzing Exponential-Family Random Graph Models by Pavel N. Krivitsky, David R. Hunter, Martina Morris, Chad Klumb

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TGNet: tensor-based graph convolutional networks for multimodal brain network analysis by Zhaoming Kong, Rong Zhou, Xinwei Luo, Songlin Zhao, Ann B. Ragin, Alex D. Leow, Lifang He

“…In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. …”
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Research Progress of Hydrological Big Data Based on CiteSpace Knowledge Graph by MENG Lu, YANG Haibo

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Graph-contrast ransomware detection (GCRD) with advanced feature selection and deep learning by Suneeta Satpathy, Pratik Kumar Swain

“…To overcome the limitations of conventional detection strategies, this study proposes the Graph-Contrast Ransomware Detection (GCRD) model comprising Graph-Based Feature Selection (GFS), Contrastive Learning (CLR), and Transformer-Based Classification (FT-Transformer + MLP). …”
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TCGCL: Complex Network Traffic Classification Algorithm Based on Graph Contrastive Learning by HU Zhongze, QIN Hongchao, LI Zhenjun, LI Yanhui, LI Ronghua, WANG Guoren

“…In addition, a traffic classification algorithm TCGCL (traffic classification graph contrastive learning) is proposed based on graph contrastive learning. …”
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ON STRUCTURED AND DIFFUSE SEISMICITY, STIFFNESS OF EARTHQUAKE FOCI, AND NONLINEARITY OF MAGNITUDE RECURRENCE GRAPHS by Evgeny G. Bugaev

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Load aggregator adjustable capability forecasting based on graph convolution neural network by DONG Lingrui, WU Binyuan

“…To this end, this paper proposes a forecasting method for load aggregator based on the graph convolution neural (GCN) network. All customers are classified into several groups according to their historical load profile, and then, tailored DR models are built for each group to construct the response sample library. …”
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Integrating IT and OT for Cybersecurity: A Stochastic Optimization Approach via Attack Graphs by Gonzalo Martinez Medina, Krystel K. Castillo-Villar, Tanveer Hossain Bhuiyan

“…This paper proposes an attack graph-based optimization model to enable cybersecure digital manufacturing. …”
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Multi-Robot exploration for coal mine rescue based on the extension of undirected graph by Linna ZHOU, Tihao WU, Xinli HUANG, Chunyu YANG, Xin ZHANG

“…Moreover, when the local graph exploration gain is insufficient, global graph exploration is performed. …”
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Three-dimensional graph reconstruction of filamentous structures from z-stack images by Oscar Sten, Emanuela Del Dottore, Marilena Ronzan, Nicola Pugno, Barbara Mazzolai

“…In this context, both morphology and topology are important features for the characterization of biological samples. Brightfield microscopy Z-stacks is one of the simplest techniques for imaging samples characterized by a given depth, and it consists of acquiring images at different focal depths. …”
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Evidence Graph Analysis of Postoperative Pain Sensitization Induced by Perioperative Sleep Deprivation by XUE Jianjun, WANG Caihong, GUO Lingling, LI Xiuxia, ZHANG Jie, XU Ziqing, HOU Huaijing, YANG Kehu

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UAV ad hoc network link prediction based on deep graph embedding by Jian SHU, Qining WANG, Linlan LIU

“…Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET.To improve the sampling efficiency, the sampling interval was calculated based on linear probability.The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training.With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment.AUC, MAP, and Error Rate were employed as evaluation indexes.The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy.…”
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Few-shot traffic classification based on autoencoder and deep graph convolutional networks by Shengwei Xu, Jijie Han, Yilong Liu, Haoran Liu, Yijie Bai

“…This zero-padding strategy poses significant challenges in traffic classification with small samples. In this paper, we propose a method based on autoencoder (AE) and deep graph convolutional networks (ADGCN) for traffic classification for few-shot datasets. …”
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The node importance evaluation method based on graph convolution in multilayer heterogeneous networks by Zhixing Chen, Jian Shu, Linlan Liu

“…Considering the diversity of node types in the network, we design an adapted node sampling method based on the meta path. An MLN node embedding model is constructed based on a graph convolutional network (MGC). …”
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Adaptive dual-graph learning joint feature selection for EEG emotion recognition by Liangliang Hu, Congming Tan, Yin Tian

“…Additionally, a graph-based semi-supervised label propagation method is employed, leveraging both global and local structural information embedded in the dual graphs to propagate emotional labels from a small subset of labeled data to unlabeled samples, thereby enabling more accurate emotion estimation in the target domain. …”
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Data augmentation based multi-view contrastive learning graph anomaly detection by LI Yifan, LI Jiayin, LIN Xingpeng, DAI Yuanfei, XU Li

“…Although contrast-based anomaly detection methods could effectively mine anomaly information based on the inconsistency of anomalous node instance pairs, avoiding the drawback of using self-coding architecture that led to the need for full graph training for the model. However, most existing contrast-based graph anomaly detection methods focused only on node-subgraph contrast patterns, ignoring the fact that the sampled node-subgraph instance pairs contained only the local information of the target node, and at the same time did not take into account the importance of each subgraph to the target node, which led to the lack of global information about the node and the emergence of the problem that the contrast patterns were too generalized. …”
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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

“…We explore the transfer learning, feature-augmented learning, and multi-target learning approaches in combination with graph neural networks, validating them on two external datasets: one with molecules similar to training data (EXT-Zamora) and one with more challenging molecules (EXT-SAMPL9). …”
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The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics. by Alexander L Lewanski, Michael C Grundler, Gideon S Bradburd

“…In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. …”
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Few-Shot Named Entity Recognition Based on the Collaborative Graph Attention Network by Haoran Niu, Zhaoman Zhong

“…Few-shot Named Entity Recognition (NER) aims to extract entity information from limited annotated samples, addressing the scarcity of data in specialized domains. …”
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