Dynamic Campus Origin–Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks

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.

Messenger RNA (mRNA) has emerged as a critical global technology that requires global joint efforts from different entities to develop a COVID-19 vaccine. However, the chemical properties of RNA pose a challenge in utilizing mRNA as a vaccine candidate. For instance, the molecules are prone to degradation, which has a negative impact on the distribution of mRNA among patients. In addition, little is known of the degradation properties of individual RNA bases in a molecule. Therefore, this study aims to investigate whether a hybrid deep learning can predict RNA degradation from RNA sequences. Two deep hybrid neural network models were proposed, namely GCN_GRU and GCN_CNN. The first model is based on graph convolutional neural networks (GCNs) and gated recurrent unit (GRU). The second model is based on GCN and convolutional neural networks (CNNs). Both models were computed over the structural graph of the mRNA molecule. The experimental results showed that GCN_GRU hybrid model outperform GCN_CNN model by a large margin during the test time. Validation of proposed hybrid models is performed by well-known evaluation measures. Among different deep neural networks, GCN_GRU based model achieved best scores on both public and private MCRMSE test scores with 0.22614 and 0.34152, respectively. Finally, GCN_GRU pre-trained model has achieved the highest AuC score of 0.938. Such proven outperformance of GCNs indicates that modeling RNA molecules using graphs is critical in understanding molecule degradation mechanisms, which helps in minimizing the aforementioned issues. To show the importance of the proposed GCN_GRU hybrid model, in silico experiments has been contacted. The in-silico results showed that our model pays local attention when predicting a given position’s reactivity and exhibits interesting behavior on neighboring bases in the sequence.

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.

Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural networks (DCNNs) has made accurate pixel-level classification tasks possible. Yet one central issue remains: the precise delineation of boundaries. Deep architectures generally fail to produce fine-grained segmentation with accurate boundaries due to their progressive down-sampling. Hence, we introduce a generic framework to overcome the issue, integrating the graph convolutional network (GCN) and deep structured feature embedding (DSFE) into an end-to-end workflow. Furthermore, instead of using a classic graph convolutional neural network, we propose a gated graph convolutional network, which enables the refinement of weak and coarse semantic predictions to generate sharp borders and fine-grained pixel-level classification. Taking the semantic segmentation of building footprints as a practical example, we compared different feature embedding architectures and graph neural networks. Our proposed framework with the new GCN architecture outperforms state-of-the-art approaches. Although our main task in this work is building footprint extraction, the proposed method can be generally applied to other binary or multi-label segmentation tasks.

Contemporary deep learning approaches exhibit state-of-the-art performance in various areas. In healthcare, the application of deep learning remains limited since deep learning methods are often considered as non-interpretable black-box models. However, the Machine Learning (ML) community made recent elaborations to develop the methods of eXplainable Artificial Intelligence (XAI). The explanation methods explain single decisions of an ML model when a single data point is fed into the model’s input. In a clinical setup, a data point can represent a single patient. Data point-specific explanations could possibly assist the need in personalized precision medicine decisions via explaining patient-specific predictions. Convolutional Neural Networks (CNNs) as deep learning methods have been already applied to classify transformed into images gene expression profiles of patients. Gene expression data can be structured by a prior knowledge molecular network (encoded as a graph) representing connections between genes. Each vertex of a molecular network is assigned a gene expression value as an attribute. The set of the attributes creates a graph signal representing a patient. Emerging field of geometric deep learning deals with methods applicable to graph structured data and extends CNNs as Graph Convolutional Neural Networks (GCNNs) classifying graph signals. Layer-wise Relevance Propagation (LRP) is a method to explain decisions of CNNs classifying image data. I extended the LRP method to make it available for GCNNs. Graph Layer-wise Relevance Propagation (GLRP) is presented as a new method to explain single decisions made by a GCNN model. In this thesis, I present a novel methodology generating patient-specific molecular subnetworks as explanations for classification decisions of an ML approach utilizing prior knowledge of molecular networks. GCNN serves as a ML approach, and its decisions are explained by developed GLRP. A sanity check of the developed GLRP method was demonstrated on a hand-written digits dataset. The biological validation was performed by applying the developed methodology to gene expression data from Human Umbilical Vein Endothelial Cells (HUVEC) treated or not treated with tumor necrosis factor alpha. To show the utility of introduced methodology in the scopes of precision medicine, it was applied to a large breast cancer dataset. The generated patient-specific subnetworks largely agree with clinical knowledge and could assist precision medicine approaches by identifying common as well as novel, and potentially druggable, drivers of tumor progression. Apart from generating patient-specific subnetworks, the developed methodology can be used as a general feature selection approach. The outcome of a feature selection approach is a subset of important for classification genes corresponding to a whole dataset. It is essential to sustain stability of selected feature subsets across different datasets with the same clinical endpoint since the selected genes are possible candidates for prognostic biomarkers. I analysed the stability of feature selection performed by GCNN+LRP. I have implemented a graph convolutional layer of GCNN as a Keras layer so that the SHapley Additive exPlanations (SHAP) method could be also applied to a Keras version of a GCNN model. The stability of feature selection performed by GCNN+LRP was compared to the stability of GCNN+SHAP and other ML-based feature selection approaches. The GCNN+LRP approach shows the highest stability. GCNN+LRP subnetworks were compared to GCNN+SHAP subnetworks in terms of connectivity and permutation feature importance. While GCNN+SHAP subnetworks demonstrate higher permutation importance than GCNN+LRP subnetworks, a GCNN+LRP subnetwork of an individual patient is on average substantially more connected and, therefore more interpretable in the context of prior knowledge than a GCNN+SHAP subnetwork which consists mainly of single vertices.

Reservoir simulation results currently provide the basis for important reservoir engineering decisions; grid complexity and non-linearity of these models demand high computational time and memory. The physics-based simulation process must be repeated to increase model prediction accuracy or to perform history matching; consequently, the simulation process is often time-consuming. This paper describes a new methodology based on a deep neural network (DNN) technique, the graph convolutional neural network (G-CNN). G-CNN increases the modeling prediction speed and efficiency by compressing the computational time and memory usage of the reservoir simulation. A G-CNN model was used to perform the reservoir simulations described. This new methodology combines physics-based and data-driven models in reservoir simulation. The workflow generates training datasets, enabling intelligent sampling of the reservoir production data in the G-CNN training process. Bottomhole pressure constraints were set for all simulations. The production data generated by the reservoir model, with the mesh connectivity information, is used to generate the G-CNN model. This approach can be viewed as hybrid data-driven, retaining the underlying physics of the reservoir simulator. The resulting G-CNN model can perform reservoir simulations for any computational grid and production time horizon. The method uses convolutional neural network and mesh connections in a fully differentiable scheme to compress the simulation state size and learns the reservoir dynamics on this compressed form. G-CNN analysis was performed on an Eagle Ford-type reservoir model. The transmissibility, pore volume, pressure, and saturation at an initial time state were used as input features to predict final pressure and saturations. Prediction accuracy of 95% was obtained by hyperparameter tuning off the G-CNN architecture. By compressing the simulation state size and learning the time-dependent reservoir dynamics on this compressed representation, reservoir simulations can be emulated by a graph neural network which uses significantly less computation and memory. The G-CNN model can be used on any computational grid because it preserves the structure of the physics. The G-CNN model is trained by mapping an initial time state to a future prediction time state; consequently, the model can be used for generalizing predictions in any grid sizes and time steps while maintaining accuracy. The result is a computationally and memory efficient neural network that can be iterated and queried to reproduce a reservoir simulation. This novel methodology combines field-scale physics-based reservoir modeling and DNN for reservoir simulations. The new reservoir simulation workflow, based on the G-CNN model, maps the time state predictions in a resampled grid, reducing computational time and memory. The new methodology presents a general method for compressing reservoir simulations, assisting in fast and accurate production forecasting.

Vehicle trajectory prediction is an important research basis for the decision making and path planning of the intelligent and connected vehicle. In the connected vehicle environment, vehicles share information and drive cooperatively, and the intelligent and connected vehicles are able to obtain more accurate and rich perception information, which provides a data basis for accurate prediction of vehicle trajectories. However, attaining accurate and effective vehicle trajectory predictions poses technical challenges due to insufficient extraction of vehicular spatial–temporal interaction features. In this paper, we propose a vehicle trajectory prediction model based on graph convolutional neural network (GCN) in a connected vehicle environment. Specifically, using the driving scene information obtained by the intelligent and connected vehicle, the spatial graph and temporal graph are constructed based on the spatial interaction coefficient (SIC) and self-attention mechanism, respectively. Then, the graph data are entered into the interaction extraction module, and the spatial interaction features and temporal interaction features are extracted separately using the graph convolutional networks, which are fused to obtain the spatial–temporal interaction information. Finally, the interaction features are learned based on the convolutional neural networks to output the future trajectory information of all vehicles in the scene by one forward operation rather than a step-by-step process. The ablation experiment results show that the method proposed in this study to model the spatiotemporal interaction among vehicles based on SIC and self-attention mechanism reduces the prediction error by 5% and 12%, respectively. The results from the model comparison experiment show that the proposed method engenders an 8% improvement in prediction accuracy over the state-of-the-art solution, providing technical and theoretical support for trajectory prediction research of intelligent and connected vehicles.

Graph convolutional neural networks have difficulties in model training takes a lot of time and lack of universality in meta-path design when processing knowledge graphs. To solve these issues, this paper proposes an efficient recommendation algorithm integrating knowledge graphs with graph convolutional networks(EKG-GCN). The algorithm is mainly based on the graph convolution algorithm. Firstly, the relationship between users and entities is standardized and scored, then a new neighborhood aggregation method is proposed. Some nodes in the knowledge graph are set as central nodes. A distance-influence function is set up according to the distance from other nodes to the central node, and the feature extraction of information in the knowledge graph is completed according to the distance-influence function. Finally, the knowledge graph features are used as an auxiliary to complete the recommendation task. In the experiment, three data sets were selected to compare with other advanced models, and it was found that the model is in an advantageous position in terms of training time and recommendation effect, and has a certain degree of universality.

Exploring the optimization of communication strategies for animation films in the context of cross-cultural communication, this research integrates the Internet of Things (IoT) and convolutional networks. The research constructs a collaborative filtering (CF) movie recommendation model based on a graph convolutional neural network (GCN) and investigates its application in cross-cultural communication. The fusion of IoT and convolutional networks in movie communication is also analyzed, and the effectiveness of the proposed GCN-CF model is validated through comparative experiments. The results indicate that, compared to other models, the GCN-CF model achieves the lowest Root Mean Square Error (RMSE) on the MovieLens 100 K and MovieLens 1 M datasets, with values of 0.8762 and 0.8275, respectively. Compared to traditional models, the GCN-CF model exhibits significantly superior performance in terms of RMSE, with reductions ranging from 0.6 to 5.2%, highlighting its heightened detection accuracy and overall performance. Moreover, the performance of the GCN-CF model is enhanced after introducing attention mechanisms and auxiliary information on both datasets, showing an improvement of 0.4% compared to the scenario without these additions. This data demonstrates the effectiveness of attention mechanisms and auxiliary information. Finally, the research presents an animation film communication strategy based on IoT and convolutional networks, offering novel insights for film production and communication, along with positive implications for cultural exchange and the advancement of the global media industry.

The objective of extracting relations between document components lies in identifying the Within a single document, the focus often lies on understanding the connections between different entities. This type of analysis goes beyond individual sentences, as it demands an understanding of how information from multiple sentences interacts to form these connections. Over recent years, the importance of exploring relationships involving several entities simultaneously has grown significantly. To advance the field of studying such connections across entire documents, a novel collection of data points, known as DocRED, has been introduced. Currently, the standard approach for this task involves using BiLSTM networks to process the entire document as a whole. However, this method struggles to effectively capture the intricate relationships that exist among various entities. To overcome this limitation, a new model designed for document-level relationship identification has been developed, which leverages Graph Convolutional Networks (GCN). GCNs are particularly useful here because they can gather information from surrounding entities, allowing for a more detailed modeling of their interactions. The proposed approach starts by identifying coreferential links to gather features that represent the relationships between pairs of entities. These features are then analyzed using GCN to construct a graph structure that represents the entire document, ultimately revealing the complex interactions between different entities. Testing this model on the large-scale DocRED dataset from Tsinghua University has demonstrated its strong performance in this challenging task.

Spectral graph convolutional neural networks (GCN) are proposed to incorporate important information contained in graphs such as gene networks. In a standard spectral GCN, there is only one gene network to describe the relationships among genes. However, for genomic applications, due to condition- or tissue-specific gene function and regulation, multiple gene networks may be available; it is unclear how to apply GCNs to disease classification with multiple networks. Besides, which gene networks may provide more effective prior information for a given learning task is unknown a priori and is not straightforward to discover in many cases. A deep multiple graph convolutional neural network is therefore developed here to meet the challenge. The new approach not only computes a feature of a gene as the weighted average of those of itself and its neighbors through spectral GCNs, but also extracts features from gene-specific expression (or other feature) profiles via a feed-forward neural networks (FNN). We also provide two measures, the importance of a given gene and the relative importance score of each gene network, for the genes' and gene networks' contributions, respectively, to the learning task. To evaluate the new method, we conduct real data analyses using several breast cancer and diffuse large B-cell lymphoma datasets and incorporating multiple gene networks obtained from "GIANT 2.0" Compared with the standard FNN, GCN, and random forest, the new method not only yields high classification accuracy but also prioritizes the most important genes confirmed to be highly associated with cancer, strongly suggesting the usefulness of the new method in incorporating multiple gene networks.

Graph neural networks (GNNs) have gradually become an important research branch in graph learning since 2005, and the most active one is unquestionably graph convolutional neural networks (GCNs). Although convolutional neural networks have successfully learned for images, voices, and texts, over-smoothing remains a significant obstacle for non-grid graphs. In particular, because of the over-smoothing problem, most existing GCNs are only effective below four layers. This work proposes a novel GCN named DII-GCN that originally integrates Dropedge, Initial residual, and Identity mapping methods into traditional GCNs for mitigating over-smoothing. In the first step of the DII-GCN, the Dropedge increases the diversity of learning sample data and slows down the network’s learning speed to improve learning accuracy and reduce over-fitting. The initial residual is embedded into the convolutional learning units under the identity mapping in the second step, which extends the learning path and thus weakens the over-smoothing issue in the learning process. The experimental results show that the proposed DII-GCN achieves the purpose of constructing deep GCNs and obtains better accuracy than existing shallow networks. DII-GCN model has the highest 84.6% accuracy at 128 layers of the Cora dataset, highest 72.5% accuracy at 32 layers of the Citeseer dataset, highest 79.7% accuracy at 32 layers of the Pubmed dataset.

Graph convolutional neural networks (GCNNs) have emerged as a promising tool in the deep learning community to learn complex hidden relationships of data generated from non-Euclidean domains and represented as graphs. GCNNs are formed by a cascade of layers of graph filters, which replace the classical convolution operation in convolutional neural networks. These graph filters, when operated over real networks, can be subject to random perturbations due to link losses that can be caused by noise, interference and adversarial attacks. In addition, these graph filters are executed by finite-precision processors, which generate numerical quantization errors that may affect their performance. Despite the research works studying the effect of either graph perturbations or quantization in GCNNs, their robustness against both of these problems jointly is still not well investigated and understood. In this paper, we propose a quantized GCNN architecture based on neighborhood graph filters under random graph perturbations. We investigate the stability of such architecture to both random graph perturbations and quantization errors. We prove that the expected error due to quantization and random graph perturbations at the GCNN output is upper-bounded and we show how this bound can be controlled. Numerical experiments are conducted to corroborate our theoretical findings.

How to efficiently exploit useful information from hyperspectral data by joint analysis of spectral and spatial information is an important problem. In this work, we demonstrate a fusion network that can leverage recent developments in graph convolutional neural networks (GCNs) to effectively analyze reflectance spectra of hyperspectral image pixels and two-dimensional convolutional neural networks (CNNs) which can extract object-specific spatial characteristics from hyperspectral image. To design this fusion model, we study both decision fusion and deep feature fusion approaches and show that the fusion approach outperforms other traditional deep learning methods such as variants of CNNs.