Multimodal feature fusion-based graph convolutional networks for Alzheimer's disease stage classification using F-18 florbetaben brain PET images and clinical indicators. by Gyu-Bin Lee, Young-Jin Jeong, Do-Young Kang, Hyun-Jin Yun, Min Yoon

“…However, few studies have applied graph neural networks to multimodal data comprising F-18 florbetaben (FBB) amyloid brain positron emission tomography (PET) images and clinical indicators. …”
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Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection by Hao Yan, Jianming Li, Lei Du, Binxing Fang, Yan Jia, Zhaoquan Gu

“…It leverages the natural graph structure of computer networks to achieve robust, multi-grained intrusion detection. …”
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Domain adaptation spatial feature perception neural network for cross-subject EEG emotion recognition by Wei Lu, Wei Lu, Xiaobo Zhang, Xiaobo Zhang, Lingnan Xia, Hua Ma, Tien-Ping Tan

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Evading control flow graph based GNN malware detectors via active opcode insertion method with maliciousness preserving by Hao Peng, Zehao Yu, Dandan Zhao, Zhiguo Ding, Jieshuai Yang, Bo Zhang, Jianming Han, Xuhong Zhang, Shouling Ji, Ming Zhong

“…Existing function-preserving adversarial attacks fall short of effectively modifying portable executable (PE) malware control flow graphs (CFGs), thereby failing to bypass the graph neural network (GNN) models that utilize CFGs for detection. …”
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Rough-and-Refine Model for Scene Graph Generation by Li Junliang, Lv Shirong, Li Wei

“…The TQG and EPR modules also provide a degree of improvement, with average decreases of 4.7% and 5.2% when removed. The model represented in the first row of the table, which excludes all four modules, is equivalent to the Rough Part, showing an average decrease of 24.9%.ConclusionsTo address the issue of insufficient predicate representation, a scene graph generation method based on a Rough-and-Refine network is proposed. …”
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Deep Reinforcement Learning-Based Routing Method for Low Earth Orbit Mega-Constellation Satellite Networks with Service Function Constraints by Yan Chen, Huan Cao, Longhe Wang, Daojin Chen, Zifan Liu, Yiqing Zhou, Jinglin Shi

“…The simulation results demonstrate that, compared with graph theory-based methods and reinforcement learning-based methods, GDRL-SFCR can reduce the end-to-end traffic transmission delay by more than 11.3%, reduce the average network load by more than 14.1%, and increase the traffic access success rate and network capacity by more than 19.1% and two times, respectively.…”
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Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion by Chenxi LIU, Dong WANG, Huiling CHEN, Renfa LI

“…By effectively capturing the spatio-temporal characteristics of urban private car travel, a multi-source heterogeneous data fusion model for private car volume prediction was proposed.Firstly, private car trajectory and area-of-interest data were integrated.Secondly, the spatio-temporal correlations between private car travel and urban areas were modeled through multi-view spatio-temporal graphs, the multi-graph convolution-attention network (MGC-AN) was proposed to extract the spatio-temporal characteristics of private car travel.Finally, the spatio-temporal characteristics and external characteristics such as weather were integrated for joint prediction.Experiments were conducted on real datasets, which were collected in Changsha and Shenzhen.The experimental results show that, compared with the existing prediction model, the root mean square error of the MGC-AN is reduced 11.3%～20.3%, and the average absolute percentage error is reduced 10.8%～36.1%.…”
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Distributed and Fault-Tolerant Routing for Borel Cayley Graphs by Junghun Ryu, Eric Noel, K. Wendy Tang

“…We explore the use of a pseudorandom graph family, Borel Cayley graph family, as the network topology with thousands of nodes operating in a packet switching environment. …”
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Frequency-band specific directed connectivity networks reveal functional disruptions and pathogenic patterns in temporal lobe epilepsy: a MEG study by Chen Zhang, Yutong Wu, Wenhan Hu, Guangfei Li, Chunlan Yang, Ting Wu

“…Directed Transfer Function (DTF) was used to construct directed connectivity networks, followed by networks and graph-theoretical analyses. …”
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A new approach to estimate neighborhood socioeconomic status using supermarket transactions and GNNs by Eduardo Cruz, Monica Villavicencio, Carmen Vaca, Lisette Espín-Noboa, Nervo Verdezoto

“…Using customer consumption data, we created a basket graph and fed it into a graph neural network to predict neighborhood socioeconomic status. …”
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Pseudo-Labeling Domain Adaptation Using Multi-Model Learning by Victor Akihito Kamada Tomita, Ricardo Marcondes Marcacini

“…We use these representations to construct a heterogeneous bipartite graph, where a neural network is employed for final classification. …”
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Screening HFC/HFO and ionic liquid for absorption refrigeration at the atomic scale by the prediction model of machine learning by Jianchun Chu, Maogang He, Georgios M. Kontogeorgis, Xiangyang Liu, Xiaodong Liang

“…This model employs the Attention E(n)-equivariant Graph Neural Network (AEGNN) applied to disconnected graphs, enabling comprehensive learning from both topological and Euclidean structural information. …”
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Graph theoretical model of a sensorimotor connectome in zebrafish. by Michael Stobb, Joshua M Peterson, Borbala Mazzag, Ethan Gahtan

“…There was high local clustering and a short average path length between nodes, implying a small-world structure similar to other neural connectomes and complex networks. …”
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CKACL: A Collaborative Knowledge-Aware Contrast Learning Framework for Addressing Sparsity and Cold-Start Problems in Recommendation System by Xintao Ma, Shuai Wu, Hao Zhang

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Trading Community Analysis of Countries’ Roll-On/Roll-Off Shipping Networks Using Fine-Grained Vessel Trajectory Data by Shichen Huang, Tengda Sun, Jing Shi, Piqiang Gong, Xue Yang, Jun Zheng, Huanshuai Zhuang, Qi Ouyang

“…We construct a method based on the complex network theory and the graph feature extraction method to quantitatively assess the features of the RO/RO shipping network. …”
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Nonparametric Bayesian Networks as a Tool of Multiscale Time Series Analysis and Remote Sensing Data Integration by Nikita S. Pyko, Denis V. Tishin, Pavel Yu. Iskandirov, Artur M. Gafurov, Bulat M. Usmanov, Mikhail I. Bogachev

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Ada-GCNLSTM: An adaptive urban crime spatiotemporal prediction model by Miaoxuan Shan, Chunlin Ye, Peng Chen, Shufan Peng

“…Specifically, in the spatial feature extraction module, we enhance the model's ability to capture crime spatial distributions by leveraging graph convolutional networks to model spatial dependencies in conjunction with the maximum mean discrepancy to extract the universal features of crime data. …”
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Heterogeneous AI Music Generation Technology Integrating Fine-Grained Control by Hongtao Wang, Li Gong

“…To tackle the persistent issue of low accuracy in current emotion recognition and music generation systems, an innovative approach was proposed that fused a graph convolutional neural network with a channel attention mechanism for emotion recognition. …”
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DMR: disentangled and denoised learning for multi-behavior recommendation by Yijia Zhang, Wanyu Chen, Fei Cai, Zhenkun Shi, Feng Qi

“…Specifically, we first design a disentangled graph convolutional network, modeling the fine-grained user preference under multiple behaviors in view of item attribute domains. …”
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A Dual-Encoder Contrastive Learning Model for Knowledge Tracing by Yanhong Bai, Xingjiao Wu, Tingjiang Wei, Liang He

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