Dynamic Hardware Defense for High-Centrality Nodes in Graph Convolutional Networks

Learning graph structure via graph convolutional networks

The automatic identification of disease types of edible mushroom crops and poisonous crops is of great significance for improving crop yield and quality. Based on the graph convolutional neural network theory, this paper constructs a graph convolutional network model for the identification of poisonous crops and edible fungi. By constructing 6 graph convolutional networks with different depths, the model uses the training mechanism of graph convolutional networks to analyze the results of disease identification and completes the automatic extraction of the disease characteristics of the poisonous crops by overfitting problem. During the simulation, firstly, the relevant PlantVillage dataset is used to obtain the pretrained model, and the parameters are adjusted to fit the dataset. The network framework is trained and parameterized with prior knowledge learned from large datasets and finally synthesized by training multiple neural network models and using direct averaging and weighting to synthesize their predictions. The experimental results show that the graph convolutional neural network model that integrates multi-scale category relationships and dense links can use dense connection technology to improve the representation ability and generalization ability of the model, and the accuracy rate generally increases by 1%–10%. The average recognition rate is about 91%, which greatly promotes the ability to identify the diseases of poisonous crops.

A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting

Graph Convolutional Networks have recently received a lot of attention for their capability of representation learning on non-Euclidian feature spaces. Graph convolutional networks aggregate the neighbouring node features and attribute to learn graph representations. Like traditional deep learning models, the representation power of graph convolutional networks also increases with the increasing number of layers. However, it also increases the difficulty associated with training such models. Deep graph convolutional networks suffer from issues like vanishing gradient or over-fitting. In this paper, we explored skip connections in graph convolutional networks and proposed a deep residual graph convolutional neural network for predicting node properties in a protein-protein interaction network. The proposed model is an improvement over traditional deep learning models and present state-of-the-art graph learning algorithms. The implementation of the algorithm, as well as the saved model, is available online for reproducibility at https://github.com/rangan2510/R-GCN.

Graph convolutional neural networks (GCNNs) have been successfully applied to a wide range of problems, including low-dimensional Euclidean structural domains representing images, videos, and speech and high-dimensional non-Euclidean domains, such as social networks and chemical molecular structures. However, in computer vision, the existing GCNNs are not provided with positional information to distinguish between graphs of new structures; therefore, the performance of the image classification domain represented by arbitrary graphs is significantly poor. In this work, we introduce how to initialize the positional information through a random walk algorithm and continuously learn the additional position-embedded information of various graph structures represented over the superpixel images we choose for efficiency. We call this method the graph convolutional network with learnable positional embedding applied on images (IMGCN-LPE). We apply IMGCN-LPE to three graph convolutional models (the Chebyshev graph convolutional network, graph convolutional network, and graph attention network) to validate performance on various benchmark image datasets. As a result, although not as impressive as convolutional neural networks, the proposed method outperforms various other conventional convolutional methods and demonstrates its effectiveness among the same tasks in the field of GCNNs.

Accumulating evidence has demonstrated that RNAs play an important role in identifying various complex human diseases. However, the number of known disease related RNAs is still small and many biological experiments are time-consuming and labor-intensive. Therefore, researchers have focused on developing useful computational algorithms to predict associations between diseases and RNAs. It is useful for people to identify complex human diseases at molecular level, especially in diseases diagnosis, therapy, prognosis and monitoring. In this paper, we propose a novel framework Graph Convolutional Attention Network(GCAN) to predict potential disease-RNAs associations. Facing thousands of associations, GCAN benefits from the efficiency of deep learning model. Compared to other disease-RNAs association prediction methods, GCAN operates the computation process from global structure of disease-RNAs network with graph convolution networks(GCN) and can also integrate local neighborhoods with the attention mechanism. What is more, GCAN is at the first attempt to utilize GCN to discover the feature representation of the latent nodes in disease-RNAs network. In order to evaluate the performance of GCAN, we conduct experiments on two different disease-RNAs networks: disease-miRNA and disease-lncRNA. Comparisons of several state-of-the-art methods using disease-RNAs networks show that our novel frameworks outperform baselines by a wide margin in potential disease-RNAs associations.

In this paper, we consider the power line outage identification problem as a graph signal classification problem, where the signal at each vertex is given as a time series. We propose graph convolutional networks (GCNs) for the task of classifying signals supported on graphs. An important element of the GCN design is filter design. We consider filtering signals in either the vertex (spatial) domain, or the frequency (spectral) domain. Two basic architectures are proposed. In the spatial GCN architecture, the GCN uses a graph shift operator as the basic building block to incorporate the underlying graph structure into the convolution layer. The spatial filter directly utilizes the graph connectivity information. It defines the filter to be a polynomial in the graph shift operator to obtain the convolved features that aggregate neighborhood information of each node. In the spectral GCN architecture, a frequency filter is used instead. A graph Fourier transform operator first transforms the raw graph signal from the vertex domain to the frequency domain, and then a filter is defined using the graph's spectral parameters. The spectral GCN then uses the output from the graph Fourier transform to compute the convolved features. There are additional challenges to classify the time-evolving graph signal as the signal value at each vertex changes over time. The GCNs are designed to recognize different spatiotemporal patterns from high-dimensional data defined on a graph. The application of the proposed methods to power line outage identification shows that these GCN architectures can successfully classify abnormal signal patterns and identify the outage location.

Dynamic Jacobi graph and trend-aware flow attention convolutional network for traffic forecasting

ObjectivesDramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome.MethodsIn total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months.ResultsBrain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age.ConclusionsBrain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome.Clinical relevance statementUnderstanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population.Key Points•Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes.•Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes.•The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

Adaptive graph convolutional clustering network with optimal probabilistic graph

Anomalies represent rare observations that vary significantly from others. Anomaly detection intended to discover these rare observations and has the power to prevent detrimental events, such as financial fraud, network intrusion, and social spam. However, conventional anomaly detection methods cannot handle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.) (Ma X, Wu J, Xue S, Yang J, Zhou C, Sheng QZ, Xiong H, Akoglu L. IEEE Trans Knowl Data Eng, 2021 [1]). Thanks to the rise of deep learning in solving these limitations, graph anomaly detection with deep learning has obtained an increasing attention from many scientists recently. However, while deep learning can capture unseen patterns of multi-dimensional Euclidean data, there is a huge number of applications where data are represented in the form of graphs. Graphs have been used to represent the structural relational information, which raises the graph anomaly detection problem—identifying anomalous graph objects (i.e., vertex, edges, sub-graphs, and change detection). These graphs can be constructed as a static graph, or a dynamic graph based on the availability of timestamp. Recent years have observed a huge efforts on static graphs, among which Graph Convolutional Network (GCN) has appeared as a useful class of models. A challenge today is to detect anomalies with dynamic structures. In this chapter, we aim at providing methods used for detecting anomalies in static and dynamic graphs using graph analysis, graph embedding, and graph convolutional neural networks. For static graphs we categorize these methods according to plain and attribute static graphs. For dynamic graphs we categorize existing methods according to the type of anomalies that they can detect. Moreover, we focus on the challenges in this research area and discuss the strengths and weaknesses of various methods in each category. Finally, we provide open challenges for graph anomaly detection using graph convolutional neural networks on dynamic graphs.KeywordsAnomaly detectionGraph anomaly detectionGraph analysisGraph embeddingGraph neural networkDynamic graphsStatic graphs

Epilepsy detection is a critical medical task, but traditional methods face challenges in accuracy and reliability due to the difficulty of EEG data acquisition and the limitation of the number of sample seizures. To overcome these challenges, this paper proposes a new model for epilepsy detection that combines Graph Convolutional Neural Network (Graph Convolutional Network, GCN) and Transformer, aiming to significantly improve the accuracy and sensitivity of detection. The core of the model adopts GCN, which utilizes its powerful inter-node relationship capturing capability and graph feature learning mechanism. However, due to the limitation of traditional GCN in integrating global features, this model incorporates the Transformer structure to enhance global feature aggregation and reduce irrelevant feature interactions. After multiple rounds of testing of the GHB-MIT dataset, the model demonstrated excellent performance, with an average sensitivity of 92.97%, specificity of 94.60%, and accuracy of 94.59%, which was significantly better than the traditional method. Further comparison with the latest literature also confirms the advantages of the present method. In summary, the epilepsy detection model we developed based on graph convolutional neural network and Transformer not only shows significant improvement in accuracy and sensitivity, but also provides more accurate and reliable technical support for epilepsy diagnosis, which provides a valuable reference for research in related fields.

Geometric deep learning provides a principled and universal way for the integration of imaging and non-imaging modes in the medical field. Graph Convolutional Networks (GCNs) have been widely explored in a variety of problems, such as disease prediction, segmentation, and matrix completion. Using large, multi-modal data sets, graphs can capture the interaction of individual elements represented as nodes in the graphs. In particular, in medical applications, nodes can represent individuals (patients or healthy controls) in a potentially large population and are accompanied by a set of features, while the edges of the graph contain the associations between subjects in an intuitive way. This representation allows the inclusion of rich imaging and non-imaging information as well as individual subject characteristics in the task of disease classification. This article gives an overview of graph convolutional neural networks, focusing on the application of graph convolutional neural networks in disease prediction, and discusses the challenges and prospects of graph convolutional neural networks in disease prediction.

Video-based human action recognition is one of the most important and challenging areas of research in the field of computer vision. Human action recognition has found many pragmatic applications in video surveillance, human-computer interaction, entertainment, autonomous driving, etc. Owing to the recent development of deep learning methods for human action recognition, the performance of action recognition has significantly enhanced for challenging datasets. Deep learning techniques are mainly used for recognizing actions in images and videos comprising of Euclidean data. A recent development in deep learning methods is the extension of these techniques to non-Euclidean data or graph data with many nodes and edges. Human body skeleton resembles a graph, therefore, the graph convolutional network (GCN) is applicable to the non-Euclidean body skeleton. In the past few years, GCN has emerged as an important tool for skeleton-based action recognition. Therefore, we conduct a survey using GCN methods for action recognition. Herein, we present a comprehensive overview of recent GCN techniques for action recognition, propose a taxonomy for the categorization of GCN techniques for action recognition, carry out a detailed study of the benchmark datasets, enlist relevant resources and open-source codes, and finally provide an outline for future research directions and trends. To the best of authors' knowledge, this is the first survey for action recognition using GCN techniques.

A BSTRACT Introduction: Oral pathogens, including bacteria, viruses, fungi, and protozoa, are crucial in dental and systemic diseases. Traditional microbiological techniques are time-consuming and limited, presenting an opportunity for advanced computational techniques such as machine learning and deep learning. Graph convolutional networks (GCNs) offer a powerful tool for understanding protein sequences and their classification based on structural and functional properties. Early detection and accurate classification of oral and periodontal pathogens can help develop targeted therapies and prevention strategies, addressing the challenge of efficiently classifying and interpreting large datasets. The study uses a graph convolutional neural network (GCN) to classify oral pathogens based on their protein sequence characteristics, a groundbreaking application of deep learning in oral microbiology. Methods: Pathogen–Host Interactions Database is a comprehensive resource for cataloging the interactions between pathogens and host organisms. Researchers use a graph convolutional network (GCN) architecture to classify protein sequences from pathogens such as Candida albicans , Porphyromonas gingivalis , and Treponema denticola , accessing data on host–bacterial interactions. GCN architecture uses 410 encoded sequences and cosine similarity to construct an adjacency matrix with 2000 encoded protein sequences, 128 neurons, 0.6 dropout rate, 64 neurons, and an output layer for pathogen species predictions. Results: The GCN performed well for Candida albicans but showed poor performance for Porphyromonas gingivalis and failed to identify Treponema denticola, indicating class-dependent limitations. Conclusion: The study uses graph-based neural networks to classify oral and periodontal pathogens based on protein sequence data. While it achieves high accuracy, it faces challenges in handling imbalanced datasets. Improvements in model architecture show promise, but limitations in class imbalance and feature representation need further research. The methodological framework can be a foundation for future pathogen classification and protein sequence analysis studies.