Neighborhood Information Research Articles

DSTSPYN: a dynamic spatial-temporal similarity pyramid network for traffic flow prediction

Traffic flow prediction plays a crucial role in intelligent transportation systems as it enables effective control and management of urban traffic. However, existing methods that based on Graph Convolutional Networks (GCNs) primarily utilize local neighborhood information for message passing, resulting in limited perception of global structures. Additionally, it is also a challenge to extract spatial-temporal similarity features due to the constraints of graph structures. To address these issues, we propose a novel traffic flow prediction model based on Dynamic Spatial-Temporal Similarity Pyramid Network (DSTSPYN). Our model employs a spatial-temporal pyramid architecture, which dynamically adjusts the weights of central, edge, and global spatial-temporal features using an enhanced attention mechanism. Furthermore, it captures dynamic temporal dependencies at different scales through pyramid gated convolution. Meanwhile, the spatial similarity features of different time steps can be extracted through the spatial-temporal global similarity (STGS) module. We evaluate our model on four public transportation datasets and demonstrate that the DSTSPYN model outperforms several baseline methods in terms of prediction accuracy. It effectively captures the dynamic spatial-temporal correlations of the road network and edge node features, making it well-suited for long-term traffic flow prediction.

Distributed strategies for pursuit-evasion of high-order integrators

This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation. Distinct error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities, accelerations and higher order information of neighbors. Consequently, our approach only requires agents to exchange position information or to measure the relative positions of the neighbors. The distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the players. Furthermore, after establishing a sufficient and necessary condition for a class of high-order integrators, we present conditions for capture and formation control with exponential convergence for three scenarios: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader. It is shown that the conditions depend on the structure of the communication graph, the weights in the control law, and the expected formation configuration. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation results.

Enhancing Short‐Term Wind Speed Prediction Capability of Numerical Weather Prediction Through Machine Learning Methods

AbstractAccurate forecasting of wind speed is essential for daily life and social production. While numerical weather prediction products are widely used, they rely on global data and mathematical models to solve atmospheric dynamics' equations, often failing to capture localized micrometeorological phenomena accurately. Factors such as surface conditions, land‐sea differences, and topography, particularly in coastal areas, further impact the accuracy of wind speed forecasts. This study presents a new method to enhance short‐term wind speed forecasting along China's coast by incorporating local and neighborhood spatiotemporal information. The approach integrates meteorological data from adjacent grid points as new inputs in the LightGBM, CatBoost, and XGBoost algorithms. Stacking ensemble technique is then employed to effectively combine with the aforementioned foundational models. Two sets of experiments are conducted: Experiments 1 exclude surrounding information, while Experiments 2 include it. Each set consists of five experiment groups: annual, spring, summer, autumn, and winter. Within each group, four models are tested: XGBoost, LightGBM, CatBoost, and stacking. Results show that incorporating surrounding site information improves forecast accuracy. In all five groups with added surrounding site information, the stacking model performs best. Compared to ECMWF forecast data, the stacking model improves wind speed forecast accuracy from 53.3%, 50.9%, 55.2%, 53.0%, and 54.0% to 77.2%, 73.1%, 76.7%, 78.2%, and 77.1%, respectively. These findings demonstrate the potential effectiveness of the proposed method for improving short‐term wind speed forecasts in China's coastal areas.

Possibilistic C-means with novel image representation for image segmentation

Image segmentation is the process of automatically dividing an image into several parts and extracting the relevant data and information. Compared to the traditional Fuzzy C-Means algorithm, the Possibilistic C-Means (PCM) algorithm has advantages in reducing the influence of noise on cluster center estimation. However, the PCM algorithm still shows poor clustering performance under high-intensity noise, which may lead to overlapping cluster centers. Considering the impact of neighborhood information of image pixels on the image segmentation results, this paper proposes a Vector-Based Possibilistic C-Means (VBPCM) algorithm. The algorithm incorporates neighborhood information and uses a vector representation method to describe image pixels. Additionally, an adjustable distance based on an exponential function is proposed to describe the similarity between vectors. The proposed VBPCM algorithm outperforms the conventional PCM, obtaining uplifiting gains of 4%, 2%, and 9% in Pixel Accuracy, Mean Pixel Accuracy, and Mean Intersection over Union, respectively. The experimental outputs illustrate that VBPCM algorithm can achieve more satisfactory cluster effect with high-intensity noise, further perform better in image segmentation task.

A Registration Method Based on Ordered Point Clouds for Key Components of Trains.

Point cloud registration is pivotal across various applications, yet traditional methods rely on unordered point clouds, leading to significant challenges in terms of computational complexity and feature richness. These methods often use k-nearest neighbors (KNN) or neighborhood ball queries to access local neighborhood information, which is not only computationally intensive but also confines the analysis within the object's boundary, making it difficult to determine if points are precisely on the boundary using local features alone. This indicates a lack of sufficient local feature richness. In this paper, we propose a novel registration strategy utilizing ordered point clouds, which are now obtainable through advanced depth cameras, 3D sensors, and structured light-based 3D reconstruction. Our approach eliminates the need for computationally expensive KNN queries by leveraging the inherent ordering of points, significantly reducing processing time; extracts local features by utilizing 2D coordinates, providing richer features compared to traditional methods, which are constrained by object boundaries; compares feature similarity between two point clouds without keypoint extraction, enhancing efficiency and accuracy; and integrates image feature-matching techniques, leveraging the coordinate correspondence between 2D images and 3D-ordered point clouds. Experiments on both synthetic and real-world datasets, including indoor and industrial environments, demonstrate that our algorithm achieves an optimal balance between registration accuracy and efficiency, with registration times consistently under one second.

Hither-CMI: Prediction of circRNA-miRNA Interactions Based on a Hybrid Multimodal Network and Higher-Order Neighborhood Information via a Graph Convolutional Network.

Numerous studies show that circular RNA (circRNA) functions as a sponge for microRNA (miRNA), significantly regulating gene expression by interacting with miRNA, which in turn affects the progression of human diseases. Traditional experimental approaches for investigating circRNA-miRNA interactions (CMI) are both time-consuming and costly, making computational methods a valuable alternative. Hence, we propose a computational model for predicting CMI, leveraging a hybrid multimodal network and higher-order neighborhood information (Hither-CMI). Specifically, Hither-CMI employs Multiple Kernel Learning (MKL) to integrate sequence, structure, and expression similarity networks of circRNA and miRNA, resulting in a hybrid multimodal network. Next, an enhanced Graph Convolutional Network (GCN) is utilized to combine the circRNA-miRNA hybrid multimodal network with the CMI association network, producing a hybrid higher-order embedding representation. Finally, the XGBoost classifier is applied for training and prediction. The Hither-CMI model achieved a predicted AUC value of 0.9134. In case studies, 25 out of the top 30 predicted CMI were confirmed by recent literature. These extensive experimental results further validate the effectiveness of Hither-CMI in predicting potential CMI, making it a promising prescreening tool for further biological research.

DFASGCNS: A prognostic model for ovarian cancer prediction based on dual fusion channels and stacked graph convolution.

Ovarian cancer is a malignant tumor with different clinicopathological and molecular characteristics. Due to its nonspecific early symptoms, the majority of patients are diagnosed with local or extensive metastasis, severely affecting treatment and prognosis. The occurrence of ovarian cancer is influenced by multiple complex mechanisms including genomics, transcriptomics, and proteomics. Integrating multiple types of omics data aids in predicting the survival rate of ovarian cancer patients. However, existing methods only fuse multi-omics data at the feature level, neglecting the shared and complementary neighborhood information among samples of multi-omics data, and failing to consider the potential interactions between different omics data at the molecular level. In this paper, we propose a prognostic model for ovarian cancer prediction named Dual Fusion Channels and Stacked Graph Convolutional Neural Network (DFASGCNS). The DFASGCNS utilizes dual fusion channels to learn feature representations of different omics data and the associations between samples. Stacked graph convolutional network is used to comprehensively learn the deep and intricate correlation networks present in multi-omics data, enhancing the model's ability to represent multi-omics data. An attention mechanism is introduced to allocate different weights to important features of different omics data, optimizing the feature representation of multi-omics data. Experimental results demonstrate that compared to existing methods, the DFASGCNS model exhibits significant advantages in ovarian cancer prognosis prediction and survival analysis. Kaplan-Meier curve analysis results indicate significant differences in the survival subgroups predicted by the DFASGCNS model, contributing to a deeper understanding of the pathogenesis of ovarian cancer and providing more reliable auxiliary diagnostic information for the prognosis assessment of ovarian cancer patients.

Slot Occupancy-Based Collision Avoidance Algorithm for Very-High-Frequency Data Exchange System Network in Maritime Internet of Things

The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the very-high-frequency (VHF) data exchange system (VDES). While the VDES increases bandwidth and data rates, ensuring the stable transmission of maritime IoT (MIoT) application data in congested coastal areas remains a challenge due to frequent collisions of AIS messages. This paper presents a slot occupancy-based collision avoidance algorithm (SOCA) for a VDES network in the MIoT. SOCA is designed to mitigate the impact of interference caused by transmissions of AIS messages on transmissions of VDE-Terrestrial (VDE-TER) data in coastal areas. To this end, SOCA provides four steps: (1) construction of the neighbor information table (NIT) and VDES frame maps, (2) construction of the candidate slot list, (3) TDMA channel selection, and (4) slot selection for collision avoidance. SOCA operates by constructing the NIT based on AIS messages to estimate the transmission intervals of AIS messages and updating VDES frame maps upon receiving VDES messages to monitor slot usage dynamically. After that, it generates a candidate slot list for VDE-TER channels, classifying the slots into interference and non-interference categories. SOCA then selects a TDMA channel that minimizes AIS interference and allocates slots with low expected occupancy probabilities to avoid collisions. To evaluate the performance of SOCA, we conducted experimental simulations under static and dynamic ship scenarios. In the static ship scenario, SOCA outperforms the existing VDES, achieving improvements of 13.58% in aggregate throughput, 11.50% in average latency, 33.60% in collision ratio, and 22.64% in packet delivery ratio. Similarly, in the dynamic ship scenario, SOCA demonstrates improvements of 7.30%, 11.99%, 39.27%, and 11.82% in the same metrics, respectively.

Energy-efficient and secure routing strategy for opportunistic data transmission in WSNs

ABSTRACT Driven by the critical importance of routing in Wireless Sensor Networks (WSNs) and the security vulnerabilities present in existing protocols, this research aims to address the key challenges in securing WSNs. Many current routing protocols focus on computational efficiency but fall short of providing strong security measures, leaving them vulnerable to malicious attacks. Reactive protocols, often preferred for their reduced bandwidth usage, heighten security concerns due to their limited resources for maintaining network routes, while proactive alternatives require more resources. Additionally, the ad hoc nature and energy limitations of WSNs make traditional security models, designed for wired and wireless networks, impractical. To overcome these limitations, this paper introduces the Secured Energy-Efficient Opportunistic Routing Scheme for sustainable WSNs. The proposed protocol is designed to enhance security by continuously updating neighbor information and verifying the validity of routing parameters, while also being power-aware, a critical factor given the energy constraints of WSNs. The protocol has been evaluated through simulation experiments, measuring key performance indicators such as throughput, average end-to-end delay (E2 delay), energy consumption, and network lifetime. The results demonstrate that the proposed protocol effectively strengthens WSN security while addressing the unique operational constraints of these networks.

Neighboring Mode‐Dependent Dynamic Event‐Triggered Control for Markov Jump Interconnected Systems With Unknown Interconnections

ABSTRACTThis work is absorbed in the neighboring mode‐dependent event‐triggered control scheme for Markov jump systems with unknown interconnections. To save communication resources and reduce cost, a dynamic event‐triggered scheme relies on neighboring modes information is proposed. Then, the state feedback controllers are designed based on neighboring mode–dependent dynamic event‐triggered approach to assure the stability of the system. Distinguished from existing results, the proposed method does not need to access the operation modes of all sub‐systems. The cyclic‐small‐gain criterion is used to tackle the unknown interconnections, thereby a novel stability criteria are obtained for the closed‐loop systems with performance. Finally, two simulation results are given to reveal the validity of the obtained results.

Research on an Echo-Signal-Detection Algorithm for Weak and Small Targets Based on GM-APD Remote Active Single-Photon Technology

Geiger-mode avalanche photodiode (GM-APD) is a single-photon-detection device characterized by high sensitivity and fast response, which enables it to detect echo signals of distant targets effectively. Given that weak and small targets possess relatively small volumes and occupy only a small number of pixels, relying solely on neighborhood information for target reconstruction proves to be difficult. Furthermore, during long-distance detection, the optical reflection cross-section is small, making signal photons highly susceptible to being submerged by noise. In this paper, a noise fitting and removal algorithm (NFRA) is proposed. This algorithm can detect the position of the echo signal from the photon statistical histogram submerged by noise and facilitate the reconstruction of weak and small targets. To evaluate the NFRA method, this paper establishes an optical detection system for remotely detecting active single-photon weak and small targets based on GM-APD. Taking unmanned aerial vehicles (UAVs) as weak and small targets for detection, this paper compares the target reconstruction effects of the peak-value method and the neighborhood method. It is thereby verified that under the conditions of a 7 km distance and a signal-to-background ratio (SBR) of 0.0044, the NFRA method can effectively detect the weak echo signal of the UAV.

Triangular Mesh Surface Subdivision Based on Graph Neural Network

Mesh subdivision is a common mesh-processing algorithm used to improve model accuracy and surface smoothness. Its classical scheme adopts a fixed linear vertex update strategy and is implemented iteratively, which often results in excessive mesh smoothness. In recent years, a nonlinear subdivision method that uses neural network methods, called neural subdivision (NS), has been proposed. However, as a new scheme, its application scope and the effect of its algorithm need to be improved. To solve the above problems, a graph neural network method based on neural subdivision was used to realize mesh subdivision. Unlike fixed half-flap structures, the non-fixed mesh patches used in this paper naturally expressed the interior and boundary of a mesh and learned its spatial and topological features. The tensor voting strategy was used to replace the half-flap spatial transformation method of neural subdivision to ensure the translation, rotation, and scaling invariance of the algorithm. Dynamic graph convolution was introduced to learn the global features of the mesh in the way of stacking, so as to improve the subdivision effect of the network on the extreme input mesh. In addition, vertex neighborhood information was added to the training data to improve the robustness of the subdivision network. The experimental results show that the proposed algorithm achieved a good subdivision of both the general input mesh and extreme input mesh. In addition, it effectively subdivided mesh boundaries. In particular, using the general input mesh, the algorithm in this paper was compared to neural subdivision through quantitative experiments. The proposed method reduced the Hausdorff distance and the mean surface distance by 27.53% and 43.01%, respectively.

Weighted mean of vectors algorithm with neighborhood information interaction and vertical and horizontal crossover mechanism for feature selection

Weighted mean of vectors algorithm with neighborhood information interaction and vertical and horizontal crossover mechanism for feature selection

Interpretable Quality Control of Sparsely Distributed Environmental Sensor Networks Using Graph Neural Networks

Abstract Environmental sensor networks play a crucial role in monitoring key parameters essential for understanding Earth’s systems. To ensure the reliability and accuracy of collected data, effective quality control (QC) measures are essential. Conventional QC methods struggle to handle the complexity of environmental data. Conversely, advanced techniques such as neural networks, are typically not designed to process data from sensor networks with irregular spatial distribution. In this study, we focus on anomaly detection in environmental sensor networks using graph neural networks, which can represent sensor network structures as graphs. We investigate its performance on two datasets with distinct dynamics and resolution: commercial microwave link (CML) signal levels used for rainfall estimation and SoilNet soil moisture measurements. To evaluate the benefits of incorporating neighboring sensor information for anomaly detection, we compare two models: graph convolution network (GCN) and a graph-less baseline: long short-term memory (LSTM). Our robust evaluation through 5-fold cross-validation demonstrates the superiority of the GCN models. For CML, the mean area under receiver operating characteristic curve for the GCN was 0.941 compared to 0.885 for the baseline-LSTM, and for SoilNet, it was 0.858 for GCN and 0.816 for the baseline-LSTM. Visual inspection of CML time series revealed that the GCN proficiently classified anomalies and remained resilient against rain-induced events often misidentified by the baseline-LSTM. However, for SoilNet, the advantage of GCN was less pronounced, likely due to inconsistent and less precise labeling. Through interpretable model analysis, we demonstrate how feature attributions vividly illustrate the significance of neighboring sensor data, particularly in distinguishing between anomalies and expected changes in signal level in the time series.

Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.

In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score. Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification. The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results. Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph- DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.

CICN: Higher-order link prediction with clustering mutual information of common neighbors

CICN: Higher-order link prediction with clustering mutual information of common neighbors

Estimation of long time-series fine-grained asset wealth in Africa using publicly available remote sensing imagery

Traditional methods for measuring asset wealth face limitations due to data scarcity, making it challenging to apply them on a large scale and over long periods with fine granularity. Publicly available satellite images, such as nighttime light imagery, have become an important alternative data source for estimating asset wealth. This study thoroughly exploited the spatial neighborhood information of nighttime light, combined with other remote sensing features and the cross-national, temporally comparable International Wealth Index (IWI), to construct long-term asset wealth estimation models for African countries with and without sample data. Based on these models, it generates asset wealth estimates for African settlements at a 500 m spatial resolution from 2012 to 2022. The R2 values for the models of countries with and without sample data are 0.85 and 0.76, respectively, with mean absolute errors of 6.08 and 8.35, and root means square errors of 8.52 and 10.81, respectively. Additionally, the accuracy of the temporal variation estimates surpasses previous related studies, achieving an R2 of 0.60. From 2012 to 2022, the overall IWI increased from 28.80 to 30.80, representing an increase of 0.11 standard deviations. In addition to countries with household survey data, the proposed method can also accurately estimate asset wealth for countries without survey data and effectively track asset wealth changes over time.

EBMGC-GNF: Efficient Balanced Multi-View Graph Clustering via Good Neighbor Fusion.

Exploiting consistent structure from multiple graphs is vital for multi-view graph clustering. To achieve this goal, we propose an Efficient Balanced Multi-view Graph Clustering via Good Neighbor Fusion (EBMGC-GNF) model which comprehensively extracts credible consistent neighbor information from multiple views by designing a Cross-view Good Neighbors Voting module. Moreover, a novel balanced regularization term based on p-power function is introduced to adjust the balance property of clusters, which helps the model adapt to data with different distributions. To solve the optimization problem of EBMGC-GNF, we transform EBMGC-GNF into an efficient form with graph coarsening method and optimize it based on accelareted coordinate descent algorithm. In experiments, extensive results demonstrate that, in the majority of scenarios, our proposals outperform state-of-the-art methods in terms of both effectiveness and efficiency.

Key Node Identification Method Based on Multilayer Neighbor Node Gravity and Information Entropy.

In the field of complex network analysis, accurately identifying key nodes is crucial for understanding and controlling information propagation. Although several local centrality methods have been proposed, their accuracy may be compromised if interactions between nodes and their neighbors are not fully considered. To address this issue, this paper proposes a key node identification method based on multilayer neighbor node gravity and information entropy (MNNGE). The method works as follows: First, the relative gravity of the nodes is calculated based on their weights. Second, the direct gravity of the nodes is calculated by considering the attributes of neighboring nodes, thus capturing interactions within local triangular structures. Finally, the centrality of the nodes is obtained by aggregating the relative and direct gravity of multilayer neighbor nodes using information entropy. To validate the effectiveness of the MNNGE method, we conducted experiments on various real-world network datasets, using evaluation metrics such as the susceptible-infected-recovered (SIR) model, Kendall τ correlation coefficient, Jaccard similarity coefficient, monotonicity, and complementary cumulative distribution function. Our results demonstrate that MNNGE can identify key nodes more accurately than other methods, without requiring parameter settings, and is suitable for large-scale complex networks.

SICGNN: Structurally informed convolutional graph neural networks for protein classification

Abstract Recently, graph neural networks (GNNs) have been widely used in various domains, including social networks, recommender systems, protein classification, molecular property prediction, and genetic networks. In bioinformatics and chemical engineering, considerable research is being actively conducted to represent molecules or proteins on graphs by conceptualizing atoms or amino acids as nodes and the relationships between nodes as edges. The overall structures of proteins and their interconnections are crucial for predicting and classifying their properties. However, as GNNs stack more layers to create deeper networks, the embeddings between nodes may become excessively similar, causing an oversmoothing problem that reduces the performance for downstream tasks. To avoid this, GNNs typically use a limited number of layers, which leads to the problem of reflecting only the local structure and neighborhood information rather than the global structure of the graph. Therefore, we propose a structurally informed convolutional GNN (SICGNN) that utilizes information that can express the overall topological structure of a protein graph during GNN training and prediction. By explicitly including information of the entire graph topology, the proposed model can utilize both local neighborhood and global structural information. We applied the SICGNN to representative GNNs such as GraphSAGE, graph isomorphism network, and graph attention network, and confirmed performance improvements across various datasets. We also demonstrate the robustness of SICGNN using multiple stratified 10-fold cross-validations and various hyperparameter settings, and demonstrate that its accuracy is comparable or better than those of existing GNN models.