The Average Lower Connectivity of Graphs by Ersin Aslan

“…It is shown that this parameter can be used to measure the vulnerability of networks. This paper contains results on bounds for the average lower connectivity and obtains the average lower connectivity of some graphs.…”
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Origin-destination prediction from road average speed data using GraphResLSTM model by Guangtong Hu, Jun Zhang

“…This article presents a novel integrated framework, effectively merging the distinctive capabilities of graph convolutional network (GCN), residual neural network (ResNet), and long short-term memory network (LSTM), hereby designated as GraphResLSTM. …”
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ECG-GraphNet: Advanced arrhythmia classification based on graph convolutional networks by Myeonghun Lee, BS, Jiwoo Lim, MS, JinKook Kim, MS

“…Objective: We propose Electrocardiogram Graph Convolutional Network (ECG-GraphNet), a graph convolutional network designed to classify arrhythmias into 3 types: normal (N), supraventricular ectopic (S), and ventricular ectopic (V) beats. …”
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An edge sensitivity based gradient attack on graph isomorphic networks for graph classification problems by Srinitish Srinivasan, Chandraumakantham OmKumar

“…Abstract Graph Neural Networks have gained popularity over the past few years. …”
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Graph Concatenations to Derive Weighted Fractal Networks by Zhanqi Zhang, Yingqing Xiao

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Graph2Mat: universal graph to matrix conversion for electron density prediction by Pol Febrer, Peter Bjørn Jørgensen, Miguel Pruneda, Alberto García, Pablo Ordejón, Arghya Bhowmik

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Edge Convolution Graph Neural Network Assisted Power Allocation for Wireless IoT Networks by Jihyung Kim, Yeji Cho, Junghyun Kim

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Hyperspectral target detection based on graph sampling and aggregation network. by Tie Li, Hongfeng Jin, Zhiqiu Li

“…The model was evaluated on seven hyperspectral image datasets, and the experimental results demonstrated that the proposed graph sampling aggregation network model could proficiently detect targets with an average detection accuracy exceeding 99 . 8%, outperforming other comparative models. …”
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Fast Nonparametric Inference of Network Backbones for Weighted Graph Sparsification by Alec Kirkley

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Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers by Dingge Liang, Junliang Li, Junping Yin

“…To address these limitations, we propose an enhanced location prediction neural network (ELP-Net) that integrates graph neural networks (GNNs) and transformers, combining the robust representation learning capabilities of GNNs with the temporal dependency modeling strength of transformers. …”
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Author name disambiguation based on heterogeneous graph neural network. by Ge Wang, Zikai Sun, Weiyang Hu, MengHuan Cai

“…As the existing graph heterogeneous neural network can not learn different types of nodes and edge interaction, add multiple attention, design ablation experiments to verify its impact on the network. …”
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Graph Neural Networks for Pressure Estimation in Water Distribution Systems by Huy Truong, Andrés Tello, Alexander Lazovik, Victoria Degeler

“…In this work, we combine physics‐based modeling and graph neural networks (GNN), a data‐driven approach, to address the pressure estimation problem. …”
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Pre-Routing Slack Prediction Based on Graph Attention Network by Jinke Li, Jiahui Hu, Yue Wu, Xiaoyan Yang

“…To address this issue, this paper proposes a timing engine based on Graph Attention Network (GAT) to predict the slack of timing endpoints. …”
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Enhancing the Efficiency of Unsupervised Network Alignment Using Quotient Graph by Lei Zhang, Feng Qian

“…Although graph neural networks (GNNs) effectively capture structural and semantic relationships, their computational intensity—stemming from multi-layer matrix operations and high memory consumption—severely limits scalability. …”
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Meta-path convolution based heterogeneous graph neural network algorithm by QIN Zhilong, DENG Kun, LIU Xingyan

“…To solve this problem, a heterogeneous graph neural network algorithm based on meta-path convolution was proposed. …”
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A Blockchain Parallel Activity Architecture with Social Network Graphs as Carriers for Internet of Things Networks by Xin Cong, Lingling Zi

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Anomaly Detection Over Multi-Relational Graphs Using Graph Structure Learning and Multi-Scale Meta-Path Graph Aggregation by Chi Zhang, Junho Jeong, Jin-Woo Jung

“…Graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. …”
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Graph neural network-based transaction link prediction method for public blockchain in heterogeneous information networks by Zening Zhao, Jinsong Wang, Jiajia Wei

“…To solve these problems, we extract transaction features to construct an Ethereum transaction heterogeneous information network (HIN) and propose a graph neural network (GNN)-based transaction prediction method for public blockchains in HINs, which can divide the network into subgraphs according to connectivity and increase the accuracy of the prediction results of transaction behavior. …”
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Aspect Sentiment Triplet Extraction with Syntax-Semantics Graph Convolutional Network by Jingyun Zhang, Shuwei Xu, Xin Gao, Zhiwei Tang

“…To address this limitation, we propose a novel Syntax-Semantics Graph Convolutional Network for aspect sentiment triplet extraction. …”
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An information dissemination strategy in social networks based on graph and content analysis by Jing Huang

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