MultiChem: predicting chemical properties using multi-view graph attention network by Heesang Moon, Mina Rho

Get full text

Reactive Power Optimization of a Distribution Network Based on Graph Security Reinforcement Learning by Xu Zhang, Xiaolin Gui, Pei Sun, Xing Li, Yuan Zhang, Xiaoyu Wang, Chaoliang Dang, Xinghua Liu

“…First, a graph-enhanced neural network is designed, to extract both topological and node-level features from the distribution network. …”
Get full text

KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis by Francesco Agnelli, Giuseppe Facchi, Giuliano Grossi, Raffaella Lanzarotti

“…This allows Graph Neural Networks (GNNs) to be applied to broader unstructured domains such as 3D face analysis. …”
Get full text

BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network by Wanwei Huang, Huicong Yu, Yingying Li, Xi He, Rui Chen

“…To address the problems of poor applicability of existing fault link recovery algorithms in network topology migration and backup path congestion, this paper proposes a backup path algorithm based on graph convolutional neural to improve deep deterministic policy gradient. …”
Get full text

A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

“…To address these issues, this work proposes a representation-learning-based graph and generative network for hyperspectral small target detection. …”
Get full text

AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction by Fang He, Fang He, Fang He, Fang He, Lian Duan, Lian Duan, Lian Duan, Lian Duan, Guodong Xing, Guodong Xing, Guodong Xing, Guodong Xing, Xiaojing Chang, Xiaojing Chang, Xiaojing Chang, Xiaojing Chang, Huixia Zhou, Huixia Zhou, Huixia Zhou, Huixia Zhou, Mengnan Yu, Mengnan Yu, Mengnan Yu, Mengnan Yu

“…However, existing methods for drug-disease association prediction still face limitations in feature representation, feature integration, and generalization capabilities.MethodsTo address these challenges, we propose a novel model named AMFGNN (Adaptive Multi-View Fusion Graph Neural Network). This model leverages an adaptive graph neural network and a graph attention network to extract drug features and disease features, respectively. …”
Get full text

Graph Sampling Through Graph Decomposition and Reconstruction Based on Kronecker Graphs by Shen Lu, Les Piegl, Richard S. Segall

“…The connectedness of the social network gives rise to a new challenge of how to efficiently sample the network and keep the graph properties and topology properties as well. …”
Get full text

Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell Network by Zhejun Kuang, Simin Liu, Jian Zhao, Liu Wang, Yunkai Li

“…Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. …”
Get full text

LGLoc as a new language model-driven graph neural network for mRNA localization by Saeedeh Akbari Rokn Abadi, Aref Shahbakhsh, Somayyeh Koohi

“…To address these limitations, we propose LGLoc, a machine learning-based approach designed to improve the accuracy of mRNA localization predictions with low computational overhead. LGLoc employs a Graph Neural Network encoder that utilizes the RNA’s secondary structure, complemented by a BERT encoder focused on the primary RNA sequence. …”
Get full text

CLB-Defense: based on contrastive learning defense for graph neural network against backdoor attack by Jinyin CHEN, Haiyang XIONG, Haonan MA, Yayu ZHENG

Get full text

GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein–Ligand Binding Affinity by Somanath Dandibhotla, Madhav Samudrala, Arjun Kaneriya, Sivanesan Dakshanamurthy

“…To overcome these limitations, we developed GNNSeq, a novel hybrid machine learning model that integrates a Graph Neural Network (GNN) with Random Forest (RF) and XGBoost. …”
Get full text

Optimal Routing in Urban Road Networks: A Graph-Based Approach Using Dijkstra’s Algorithm by Zarko Grujic, Bojana Grujic

“…This paper presents a new approach to optimizing route selection in urban road networks with sparsely placed traffic counters. By leveraging graph theory and Dijkstra’s algorithm, we propose a new method to determine the shortest path between origins and destinations in city traffic networks with sparsely placed counters. …”
Get full text

Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT) by Namwoo Kim, Yoonjin Yoon

“…It simultaneously learns multiple objectives of spatial and human activity variations through a heterogeneous graph attention network. Results: Experiments conducted on data from New York City show that HUGAT outperforms state-of-the-art models across various prediction tasks, including average personal income, poverty ratio, region popularity, and spatial clustering. …”
Get full text

Traffic flow prediction based on spatial-temporal multi factor fusion graph convolutional networks by Ying-Ting Chen, An Liu, Cheng Li, Shuang Li, Xiao Yang

“…To address the above issues, we proposed a spatial-temporal multi factor fusion graph convolution network (STFGCN), which is composed of multi factor graph fusion module, the GCN based on the auto-regressive moving average (ARMA) filter and the gated recurrent unit (GRU). …”
Get full text

Comparison of classical, xgboost and neural network methods for parameter estimation in epidemic processes on random graphs by Ágnes Backhausz, Edit Bognár, Villő Csiszár, Damján Tárkányi, András Zempléni

“…Since we model the underlying social network by flexible two-layer random graphs, we can also study how the structural difference between the graphs in the training set and the test set influences the error of the estimate. …”
Get full text

Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning by Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen

“…This paper presents an advanced graph convolutional network model, enhanced with Wasserstein distance-based adversarial learning (WD-ACGN), addressing the limitations of existing single-station and less explored multi-station water level forecasting approaches. …”
Get full text

Interpersonal Relationship Detection Using Multi-Head Graph Attention Networks With Multi-Feature Fusion by Simge Akay, Duygu Cakir, Nafiz Arica

“…This paper presents a novel Multi-Head Graph Attention Network (MHF-GAT) with Multi-Feature Fusion for interpersonal relationship detection (IRD) from images. …”
Get full text

An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

Get full text

Ensemble Network Graph-Based Classification for Botnet Detection Using Adaptive Weighting and Feature Extraction by Muhammad Aidiel Rachman Putra, Tohari Ahmad, Dandy Pramana Hostiadi, Royyana Muslim Ijtihadie

“…Network flows are represented in a graph with IP addresses as vertices and communication links between IP addresses as edges. …”
Get full text

Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency by Renjie Xu, Zhanlue Liang, Dan Wang, Rui Zhang, Jiayi Li, Lingfeng Bi, Kai Zhang, Weimin Li

“…Graph neural networks could compare the difference among all samples (nodes in graph) and transmit the interrelationship among them to obtain a global landscape. …”
Get full text