Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Physical information-enhanced graph neural network for predicting phase separation

Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt numbers.The results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

Graph neural network-based scheduling for multi-UAV-enabled communications in D2D networks

In this paper,we jointly design the power control and position dispatch for Multi-Unmanned Aerial Vehicle(UAV)-enabled communication in Device-to-Device(D2D)networks.Our objective is to maximize the total transmission rate of Downlink Users(DUs).Meanwhile,the Quality of Service(QoS)of all D2D users must be satisfied.We comprehensively considered the interference among D2D communications and downlink transmissions.The original problem is strongly non-convex,which requires high computational complexity for traditional optimization methods.And to make matters worse,the results are not necessarily globally optimal.In this paper,we propose a novel Graph Neural Networks(GNN)based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner.Particularly,we first construct a GNN-based model for the proposed network,in which the transmission links and interference links are formulated as vertexes and edges,respectively.Then,by taking the channel state information and the coordinates of ground users as the inputs,as well as the location of UAVs and the transmission power of all transmitters as outputs,we obtain the mapping from inputs to outputs through training the parameters of GNN.Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples.Moreover,it also shows that the performance of proposed GNN-based method is better than that of traditional means.

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

HGNN-ETC: Higher-Order Graph Neural Network Based on Chronological Relationships for Encrypted Traffic Classification

Encrypted traffic plays a crucial role in safeguarding network security and user privacy.However,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic essential.Existing methods for detecting encrypted traffic face two significant challenges.First,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic.Second,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential characteristics.To address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed HGNN-ETC.This approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context information.HGNN-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate classification.We select the ISCXVPN and the USTC-TK2016 dataset for our experiments.The results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.

Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

Heterophilic Graph Neural Network Based on Spatial and Frequency Domain Adaptive Embedding Mechanism

Graph Neural Networks(GNNs)play a significant role in tasks related to homophilic graphs.Traditional GNNs,based on the assumption of homophily,employ low-pass filters for neighboring nodes to achieve information aggregation and embedding.However,in heterophilic graphs,nodes from different categories often establish connections,while nodes of the same category are located further apart in the graph topology.This characteristic poses challenges to traditional GNNs,leading to issues of“distant node modeling deficiency”and“failure of the homophily assumption”.In response,this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks(SFA-HGNN),which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned issues.Specifically,for the first problem,we propose the“Distant Spatial Embedding Module”,aiming to select and aggregate distant nodes through high-order randomwalk transition probabilities to enhance modeling capabilities.For the second issue,we design the“Proximal Frequency Domain Embedding Module”,constructing adaptive filters to separate high and low-frequency signals of nodes,and introduce frequency-domain guided attention mechanisms to fuse the relevant information,thereby reducing the noise introduced by the failure of the homophily assumption.We deploy the SFA-HGNN on six publicly available heterophilic networks,achieving state-of-the-art results in four of them.Furthermore,we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation,demonstrating a positive correlation between“node structural similarity”,“node attribute vector similarity”,and“node homophily”in heterophilic networks.

Calculating real-time surface deformation for large active surface radio antennas using a graph neural network

This paper presents an innovative surrogate modeling method using a graph neural network to compensate for gravitational and thermal deformation in large radio telescopes.Traditionally,rapid compensation is feasible for gravitational deformation but not for temperature-induced deformation.The introduction of this method facilitates real-time calculation of deformation caused both by gravity and temperature.Constructing the surrogate model involves two key steps.First,the gravitational and thermal loads are encoded,which facilitates more efficient learning for the neural network.This is followed by employing a graph neural network as an end-to-end model.This model effectively maps external loads to deformation while preserving the spatial correlations between nodes.Simulation results affirm that the proposed method can successfully estimate the surface deformation of the main reflector in real-time and can deliver results that are practically indistinguishable from those obtained using finite element analysis.We also compare the proposed surrogate model method with the out-of-focus holography method and yield similar results.

Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.

Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification

With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current research.The graph neural network(GNN)has emerged as an approach to semi-supervised classification,and the application of GNN to hyperspectral images has attracted much attention.However,in the existing GNN-based methods a single graph neural network or graph filter is mainly used to extract HSI features,which does not take full advantage of various graph neural networks(graph filters).Moreover,the traditional GNNs have the problem of oversmoothing.To alleviate these shortcomings,we introduce a deep hybrid multi-graph neural network(DHMG),where two different graph filters,i.e.,the spectral filter and the autoregressive moving average(ARMA)filter,are utilized in two branches.The former can well extract the spectral features of the nodes,and the latter has a good suppression effect on graph noise.The network realizes information interaction between the two branches and takes good advantage of different graph filters.In addition,to address the problem of oversmoothing,a dense network is proposed,where the local graph features are preserved.The dense structure satisfies the needs of different classification targets presenting different features.Finally,we introduce a GraphSAGEbased network to refine the graph features produced by the deep hybrid network.Extensive experiments on three public HSI datasets strongly demonstrate that the DHMG dramatically outperforms the state-ofthe-art models.

Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

System logs are essential for detecting anomalies,querying faults,and tracing attacks.Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection,it cannot meet the actual needs.The implementation of automated log anomaly detection is a topic that demands urgent research.However,the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data.Meanwhile,there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection.This paper proposes a novel and practical detection model named LCC-HGLog,the core of which is the conversion of log anomaly detection into a graph classification problem.Semantic temporal graphs(STG)are constructed by extracting the raw logs’execution sequences and template semantics.Then a unique graph classifier is used to better comprehend each STG’s semantic,sequential,and structural features.The classification model is trained jointly by graph classification loss and label contrastive loss.While achieving discriminability at the class-level,it increases the fine-grained identification at the instance-level,thus achieving detection performance even with a small amount of labeled data.We have conducted numerous experiments on real log datasets,showing that the proposed model outperforms the baseline methods and obtains the best all-around performance.Moreover,the detection performance degrades to less than 1%when only 10%of the labeled data is used.With 200 labeled samples,we can achieve the same or better detection results than the baseline methods.

Modeling Price-Aware Session-Based Recommendation Based on Graph Neural Network

Session-based Recommendation(SBR)aims to accurately recom-mend a list of items to users based on anonymous historical session sequences.Existing methods for SBR suffer from several limitations:SBR based on Graph Neural Network often has information loss when constructing session graphs;Inadequate consideration is given to influencing factors,such as item price,and users’dynamic interest evolution is not taken into account.A new session recommendation model called Price-aware Session-based Recommendation(PASBR)is proposed to address these limitations.PASBR constructs session graphs by information lossless approaches to fully encode the original session information,then introduces item price as a new factor and models users’price tolerance for various items to influence users’preferences.In addition,PASBR proposes a new method to encode user intent at the item category level and tries to capture the dynamic interest of users over time.Finally,PASBR fuses the multi-perspective features to generate the global representation of users and make a prediction.Specifically,the intent,the short-term and long-term interests,and the dynamic interests of a user are combined.Experiments on two real-world datasets show that PASBR can outperform representative baselines for SBR.

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient.This research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured data.Every node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph structures.Due to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and healthcare.Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies.The Graph-based deep learning networks are designed to predict unknown objects and outliers.In our case,they detect unusual objects in the form of malicious nodes.The edges between nodes represent a relationship of nodes among each other.In case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an anomaly.The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome.Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance videos.Performing autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places.The suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniques.

Application of graph neural network and feature information enhancement in relation inference of sparse knowledge graph

At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.

A Graph Neural Network Recommendation Based on Long-and Short-Term Preference

The recommendation system(RS)on the strength of Graph Neural Networks(GNN)perceives a user-item interaction graph after collecting all items the user has interacted with.Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession.However,user preferences are dynamic.With the passage of time and some trend guidance,users may generate some short-term preferences,which are more likely to lead to user-item interactions.A GNN recommendation based on long-and short-term preference(LSGNN)is proposed to address the above problems.LSGNN consists of four modules,using a GNN combined with the attention mechanism to extract long-term preference features,using Bidirectional Encoder Representation from Transformers(BERT)and the attention mechanism combined with Bi-Directional Gated Recurrent Unit(Bi-GRU)to extract short-term preference features,using Convolutional Neural Network(CNN)combined with the attention mechanism to add title and description representations of items,finally inner-producing long-term and short-term preference features as well as features of items to achieve recommendations.In experiments conducted on five publicly available datasets from Amazon,LSGNN is superior to state-of-the-art personalized recommendation techniques.

Collision-aware interactive simulation using graph neural networks

Deep simulations have gained widespread attention owing to their excellent acceleration performances.However,these methods cannot provide effective collision detection and response strategies.We propose a deep interac-tive physical simulation framework that can effectively address tool-object collisions.The framework can predict the dynamic information by considering the collision state.In particular,the graph neural network is chosen as the base model,and a collision-aware recursive regression module is introduced to update the network parameters recursively using interpenetration distances calculated from the vertex-face and edge-edge tests.Additionally,a novel self-supervised collision term is introduced to provide a more compact collision response.This study extensively evaluates the proposed method and shows that it effectively reduces interpenetration artifacts while ensuring high simulation efficiency.

Subgraph Matching Using Graph Neural Network

Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph may contain many subgraphs isomorphic to a given target graph. In this paper GNN is modeled to identify a subgraph that matches the target graph along with its characteristics. The simulation results show that GNN is capable of identifying a target sub-graph in a graph.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics.AOX-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic agents.Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability.In this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism.AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks.AOMP significantly outperformed the benchmark methods in both cross-validation and external testing.Using AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro experiments.Furthermore,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://aomp.alphama.com.cn.

STGNN-LMR:A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition

Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal(sEMG)as input and apply a machine learning classifier or Back Propagation Neural Network(BPNN)for classification.However,this artificial feature engineering technique is not generalizable to similar tasks and is heavily reliant on the researcher’s subject expertise.In contrast,neural networks such as Convolutional Neural Network(CNN)and Long Short-term Memory Neural Network(LSTM)can automatically extract features,providing a more generalized and adaptable approach to lower limb motion recognition.Although this approach overcomes the limitations of human feature engineering,it may ignore the potential correlation among the sEMG channels.This paper proposes a spatial–temporal graph neural network model,STGNN-LMR,designed to address the problem of recognizing lower limb motion from multi-channel sEMG.STGNN-LMR transforms multi-channel sEMG into a graph structure and uses graph learning to model spatial–temporal features.An 8-channel sEMG dataset is constructed for the experimental stage,and the results show that the STGNN-LMR model achieves a recognition accuracy of 99.71%.Moreover,this paper simulates two unexpected scenarios,including sEMG sensors affected by sweat noise and sudden failure,and evaluates the testing results using hypothesis testing.According to the experimental results,the STGNN-LMR model exhibits a significant advantage over the control models in noise scenarios and failure scenarios.These experimental results confirm the effectiveness of the STGNN-LMR model for addressing the challenges associated with sEMG-based lower limb motion recognition in practical scenarios.

Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks

Fueled by the explosive growth of ultra-low-latency and real-time applications with specific computing and network performance requirements,the computing force network(CFN)has become a hot research subject.The primary CFN challenge is to leverage network resources and computing resources.Although recent advances in deep reinforcement learning(DRL)have brought significant improvement in network optimization,these methods still suffer from topology changes and fail to generalize for those topologies not seen in training.This paper proposes a graph neural network(GNN)based DRL framework to accommodate network trafic and computing resources jointly and efficiently.By taking advantage of the generalization capability in GNN,the proposed method can operate over variable topologies and obtain higher performance than the other DRL methods.