Narrow Results

With the increasing complexity of financial markets, it is gradually becoming a research hotspot in the field of accurate stock market prediction. The ant lion optimization algorithm, due to its excellent global search ability and the advantages of machine learning models in data mining, aims to further improve the accuracy of stock market prediction. Therefore, this paper will explore how to combine the ant lion optimization algorithm with machine learning models to achieve effective prediction of stock market trends. First, a stock relationship graph is constructed using graph neural networks, with stocks as nodes and relationships between stocks as edges. The node embedding technique of graph neural networks is used to extract feature representations of stocks. Then, the ant lion algorithm is used to optimize the parameters of the graph neural network, enabling it to better fit the historical data of the stock market. Finally, the trained model is used to predict the future trend of the stock market. Through experimental verification, it has been found that the proposed method has high fitness during the training process and short training time; the loss value of ant lion’s optimized machine learning algorithm is relatively small, and the model prediction accuracy is high. Applying this method to Apple, Facebook, and Tesla results in higher strategic trading returns. This method improves the accuracy of stock market prediction by optimizing machine learning algorithms with ant lions, providing investors with more reliable decision support.

As the scale of the e-commerce market continues to expand, the number of products is growing rapidly. How to efficiently obtain products that meet demand has become a major concern for e-commerce merchants and users. The product recommendation system can spontaneously find products that best match user preferences, and is an important method to solve the “information overload” problem of e-commerce platforms. As user needs are more closely integrated with recommendation models, recommendation performance is also facing higher requirements. How to further improve the accuracy of system recommendation is a continuing concern in academia and industry. Most of the existing recommendation algorithms based on deep learning only consider a single type of user behavior, ignoring the multiple types of behaviors that may occur when users browse products, such as viewing, adding to shopping cart and purchasing. These different behaviors contain a large amount of user preference information that is useful for recommendations. In order to consider the dependencies between various types of user behaviors in a fine-grained manner, we propose a multi-behavior product recommendation model, referred to as IBDM, that integrates behavioral dependencies into a multi-task learning framework. The IBDM model learns separate interaction functions for each behavior type, introduces a gating mechanism to adaptively learn the relationship between behaviors according to the actual situation, and introduces a tower structure for each behavior to output the user’s predicted value under that behavior. We conducted experiments on two real-world datasets to verify the recommendation effect of the IBDM model. Compared with the classic recommendation model, the IBDM model improved both HR and NDCG indicators.

The continuous development of society is accelerating the process of urbanization and promoting the rapid construction of smart cities. As an important part of smart cities, many cities have begun to establish intelligent transportation systems (ITS). As the cornerstone of ITS, traffic flow prediction technology is crucial in trip prediction and urban traffic management. How to more effectively capture the spatiotemporal information present in traffic data is the fundamental problem of traffic flow prediction. However, existing methods have problems such as insufficient mining of spatiotemporal features and incomplete modeling of spatiotemporal relationships when performing traffic flow prediction tasks. Aiming at the problem that the existing study approaches lack dynamic modeling of traffic data, and RNN-based approaches have limitations in the capture of global information, this study proposes a self-attention-based spatial-temporal double graph convolutional networks for traffic flow forecasting (SASTDGCN). This method abstracts the influence between nodes in the time domain into a graph structure with relationships, and then uses relational graph convolutional neural networks (RGCN) for extraction of temporal features, so that road network nodes can directly obtain the traffic conditions of nodes at historical moments that are far apart, effectively addressing long-term forecasting. In addition, the model utilizes a module that is based on a dual-domain temporal and spatial self-concern mechanism for capturing the association between spatial and historical time steps of road nodes and generates a corresponding dynamic graph structure for the road network at each time step and the historical time steps of each node, successfully improving the model prediction accuracy by 13%.

Image detection is to accurately locate the object in the image and classify the object. Graph neural network is a kind of deep neural network based on graph data structure. In recent years, with the rapid development of artificial intelligence technology, image object detection methods based on deep learning have emerged in an endless stream. Although some studies have improved the association expression of images, there is no effective mining of the association between objects and categories, resulting in limited detection accuracy. Therefore, this project intends to study a new object detection method based on graph-relation inference. On this basis, an attention-based approach is proposed to model the dependencies between candidate regions and tags. First, the extracted candidate regions are classified. On this basis, the present study proposes an autonomous attention mechanism to model and analyze the interdependencies among diverse data sets, and to learn the interdependence between the two sets through the interactive attention model. Finally, based on the similarity between the two new sets, the classification results are predicted and used in edge regression analysis to further improve the performance of the image object detection algorithm. Experimental results show that compared with the baseline model, the proposed method has a 1.6% increase in mean accuracy (mAP) on PascalVOC dataset and a 1.7% increase in mean accuracy (AP) on MS-COCO dataset, demonstrating the effectiveness and superiority of the proposed method in target detection tasks.

Influence Maximization (IM) involves identifying a limited number of high-influence nodes within a network to maximize the number of influenced nodes. Although graph neural network-based IM methods have significantly improved generalization capability and propagation effect compared to traditional methods, they face challenges in capturing features of sparse graph networks and difficulties in computing gradient descent, leading to suboptimal diffusion effects and difficult model training. To address these issues, a Graph Attention Weight Fusion-based Influence Maximization method (GAWF) is proposed. First, the GAWF integrates adaptive weight decay optimization using AdamW with graph attention weight fusion. Second, inspired by the Beta-VAE in the CV field, an IM-VAE encoder method tailored for the IM problem is introduced. Finally, extensive comparative analysis experiments are conducted on four real datasets, including Jazz, Cora_ML and Power_Grid, to evaluate three traditional and four learning-based IM algorithms. Experimental results consistently show that the proposed GAWF method achieves a 0.1%$\sim$6% improvement across various datasets, with more significant enhancements on sparser datasets, indicating that GAWF is reasonable and effective. Additionally, the proposed GAWF method holds promising applications in real-world scenarios such as social networks and public opinion analysis.

With the rapid development of the smart grid, the fast and accurate fault location of low-voltage distribution networks has become the key to ensuring the stability and reliability of the power supply. This paper aims to explore and construct a fault location model of low-voltage distribution network based on an attention diagram neural network. First, this paper analyzes the current situation and challenges of fault location in low-voltage distribution network, and points out that traditional methods have limitations when processing large-scale and high-dimensional power system data. Subsequently, a graph neural network (GNN) is introduced for processing graph-structured network data, and combined with attention mechanisms. Thus, an innovative attention-graph neural network model (named as A-GNN) is proposed for the purpose. The model can make full use of the topology structure and node feature information in the power grid, and dynamically adjust the information aggregation weight between different nodes through the attention mechanism. This is expected to achieve efficient and accurate fault location. In the experimental part, we trained and tested the A-GNN model based on the real low-voltage distribution network dataset, and compared it with several prediction models. The experimental results show that the A-GNN model has higher accuracy and recall rate in fault location tasks, especially in complex fault scenarios.

With the advent of the information explosion era, how to obtain the core content of long text quickly and accurately has become a research hotspot. With traditional automatic summarization methods, it is often difficult to capture the deep semantic relationships and long-distance dependencies in the text, resulting in poor quality of the generated summarization. In this paper, based on Graph Neural Network (GNN) and Bidirectional Encoder Representations from Transformers (BERT), an automatic summarization model for long text is constructed. First, long text is divided into text embedding representation to obtain rich context information. The hierarchical decomposition position embedding and Convolutional Neural Network (CNN) are adopted to pre-train BERT model. Furthermore, in order to capture the global and local semantic perception information of text, the GNN model based on topic algorithm and BiLSTM is integrated to construct a new model that can accurately identify the overall structure and context of text and realize more accurate automatic summarization of long text. Extensive evaluation was performed simultaneously on multiple long text datasets and compared with other summarization methods. The experimental results show that the proposed method can significantly improve the performance of the long text automatic summarization task, which not only improves the accuracy of the summarization, but also better preserves the key information in the original text. The research results not only provide a new technical path for the field of automatic summarization, but also provide a new idea for long text processing and analysis.

Graph Edit Distance (GED) computation is a fundamental yet NP-hard problem in graph theory that quantifies the structural dissimilarity between graphs through a series of edit operations. Despite its significance in fields like bioinformatics, cheminformatics, and social network analysis, the computational complexity of exact GED calculation has driven the development of heuristic and approximate methods. This paper proposes a novel approach leveraging Graph Neural Networks to predict GED efficiently. By capturing both local and global graph features through advanced embedding techniques and integrating the Weisfeiler–Lehman graph kernel, our method achieves high accuracy in estimating GED values. Extensive experiments on datasets such as AIDS, Linux, and IMDB demonstrate that our approach outperforms existing methods in terms of mean absolute error (MAE) and computational feasibility. The proposed framework not only enhances the scalability and precision of GED computation, but also provides a robust tool for graph dissimilarity assessments in various application domains.

Trajectory prediction of unmanned swarm systems, serving as the foundation for behavioral and intentional cognition, has attracted extensive attention and made considerable progress in adversarial research. The influence of heterogeneous interaction relationships and external factors is crucial for trajectory prediction. Consequently, this paper proposes the Heterogeneous Graph with External Embedding (HGEE) network. We model the latent variables as multi-layer heterogeneous graphs based on prior knowledge of different interaction relationships and propose a method for calculating edge embeddings for heterogeneous graphs. Furthermore, we introduce a method that combines external environmental feature with historical observational trajectory data as the input for the decoder, enabling the model to learn the impacts of obstacles, targets, and desired formations on trajectories. We demonstrate that our approach surpasses state-of-the-art models in interaction inference and trajectory prediction through experiments on our proposed formation datasets based on consensus theory, across five evaluation metrics.

Constructing computational decoding models to account for the cortical representation of semantic information plays a crucial role in understanding visual perception. The human visual system processes interactive relationships among different objects when perceiving the semantic contents of natural visions. However, the existing semantic decoding models commonly regard categories as completely separate and independent visually and semantically and rarely consider the relationships from prior information. In this work, a novel semantic graph learning model was proposed to decode multiple semantic categories of perceived natural images from brain activity. The proposed model was validated on the functional magnetic resonance imaging data collected from five normal subjects while viewing 2750 natural images comprising 52 semantic categories. The results showed that the Graph Neural Network-based decoding model achieved higher accuracies than other deep neural network models. Moreover, the co-occurrence probability among semantic categories showed a significant correlation with the decoding accuracy. Additionally, the results suggested that semantic content organized in a hierarchical way with higher visual areas was more closely related to the internal visual experience. Together, this study provides a superior computational framework for multi-semantic decoding that supports the visual integration mechanism of semantic processing.

Visual semantic decoding aims to extract perceived semantic information from the visual responses of the human brain and convert it into interpretable semantic labels. Although significant progress has been made in semantic decoding across individual visual cortices, studies on the semantic decoding of the ventral and dorsal cortical visual pathways remain limited. This study proposed a graph neural network (GNN)-based semantic decoding model on a natural scene dataset (NSD) to investigate the decoding differences between the dorsal and ventral pathways in process various parts of speech, including verbs, nouns, and adjectives. Our results indicate that the decoding accuracies for verbs and nouns with motion attributes were significantly higher for the dorsal pathway as compared to those for the ventral pathway. Comparative analyses reveal that the dorsal pathway significantly outperformed the ventral pathway in terms of decoding performance for verbs and nouns with motion attributes, with evidence showing that this superiority largely stemmed from higher-level visual cortices rather than lower-level ones. Furthermore, these two pathways appear to converge in their heightened sensitivity toward semantic content related to actions. These findings reveal unique visual neural mechanisms through which the dorsal and ventral cortical pathways segregate and converge when processing stimuli with different semantic categories.

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Over the past few years, graph contrastive learning (GCL) has gained great success in processing unlabeled graph-structured data, but most of the existing GCL methods are based on instance discrimination task which typically learns representations by minimizing the distance between two versions of the same instance. However, different from images, which are assumed to be independently and identically distributed, graphs present relational information among data instances, in which each instance is related to others by links. Furthermore, the relations are heterogeneous in many cases. The instance discrimination task cannot make full use of the relational information inherent in the graph-structured data. To solve the above-mentioned problems, this paper proposes a relation-aware graph contrastive learning method, called RGCL. Aiming to capture the most important heterogeneous relations in the graph, RGCL explicitly models the edges, and then pulls semantically similar pairs of edges together and pushes dissimilar ones apart with contrastive regularization. By exploiting the full potential of the relationship among nodes, RGCL overcomes the limitations of previous GCL methods based on instance discrimination. The experimental results demonstrate that the proposed method outperforms a series of graph contrastive learning frameworks on widely used benchmarks, which justifies the effectiveness of our work.

With the explosively growing amount of online information, recommender system becomes an important tool to help users efficiently find their desired information. In this paper, we propose a Graph Neural Network Social Recommendation Based on Coupled Influence by analyzing the social influence of 2-level friends (CI-GNNSR). First, we mine the user’s historical rating information and second-degree social information. Then, to learn the feature representation of users and movies, multiple Graph Attention Networks (GAT) are used to model the user-movie Graph and social network Graph. Our algorithm uses an attention-based memory network to learn the interest influence representation between users and their collaborative friends, which can distinguish the related factors among different users’ friends. The experiment results show that CI-GNNSR enhances the accuracy of recommendation by considering users’ social influence factors from multiple perspectives.

In recent years, the study of social-based recommender systems has become an active research topic. We incorporate a combination of static and dynamic interest characteristics to predict users’ real-time dynamic interests, which has rarely been considered in previous studies. In this paper, we propose a graph neural network social recommendation model that integrates static and dynamic feature relationships (FSDFR-GNNSR). The model uses a graph embedding algorithm to extract static features of users and movies, and takes the static features as input to gated recurrent unit (GRU), so that the model can take static features into consideration while modeling user dynamic behavior. Finally, we use graph attention networks to represent the dynamic influence of friends, simplify the update strategy of second-order neighbor nodes. We apply graph pooling operations to improve the generalization ability of the algorithm. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches.

Fault classification of mechanical equipment is a vital issue in modern industrial production. Mechanical equipment typically relies on multiple sensors to collect the operational data, which is represented as multivariate time-series data. However, existing methods for analyzing mechanical faults often overlook the causal relationships between sensors and struggle with the scarcity of labeled training samples. To address these challenges, we propose a graph neural network model leveraging sensor causality and meta-learning for mechanical fault classification (SCML-GNN). Specifically, we use transfer entropy to represent multivariate time-series data as a graph, with each sensor as a node and their causal relationships as edges. We then extract the node features using temporal convolutional layers and apply a graph neural network to learn the low-dimensional features. Additionally, graph pooling methods are used to obtain global embeddings. To further tackle the issue of limited labeled training samples, we introduce a metric-based class prototype attention mechanism within SCML-GNN. Extensive experiments conducted on three real-world mechanical equipment datasets demonstrate the superior effectiveness and efficiency of SCML-GNN in mechanical fault classification compared to the other existing methods.

With the rapid development of Internet technology, people can learn all kinds of travel information anytime and anywhere. However, the serious information overload causes travelers to be unable to make accurate and reasonable travel routes that meet tourists’ tastes for a while, thus reducing the quality of travel. The recommendation system as the mainstream solution to the information explosion of two means has received the attention of the majority of scholars and industry. Based on the research theory of tourist route recommendation, this paper analyzes the characteristics of attractions, factors affecting travelers’ travel experience when touring attractions and factors affecting travelers’ travel experience along tourist routes. Furthermore, we propose a tourist route recommendation model that meets tourists’ preferences. Then, this paper uses the graph neural network (GNN) algorithm to build a framework for tourist route recommendations based on the GNN using the relationship of preference and commonality existing among groups, tourists and attractions. The GNN algorithm is optimized and improved using multiple graphs and an attention mechanism. Finally, the effectiveness of this paper’s algorithm is verified by conducting experiments on different data sets.

With the prevalence of artificial intelligence technologies, big data has been utilized to higher extent in many cross-domain fields. This paper concentrates on the digital assessment of vocational education level in some specific areas, and proposes a graph neural network-based assessment model for this purpose. Assume that all vocational colleges inside a specific region are with a social graph, in which each college is a node and the relations among them are the edges. The graph neural network (GNN) model is formulated to capture global structured features of all the nodes together. The GNN is then employed for the sequential modeling pattern, and the evolving characteristics of all the colleges can be captured. Some experiments are also conducted to evaluate the performance of the proposed GNN-VEL. It is compared with two typical forecasting methods under evaluation of two metrics. The results show that it performs better than other two methods.

Considerable advances have been made recently in applying reinforcement learning (RL) to packing problems. However, most of these methods lack scalability and cannot be applied in dynamic environments. To address this research gap, we propose a hybrid algorithm called GraphPack to solve the strip packing problem. Two graph neural networks are designed to fully incorporate the problem’s structure and enhance generalization performance. SkylineNet encodes the geometry of free space as the context feature, while PackNet, supporting the symmetry of rectangles, chooses the next rectangle to pack from the remaining rectangles at each timestep. We conduct fixed-scale, variable rectangle number and variable strip width experiments to test our method. The experimental results show that our method outperforms classical heuristic methods as well as previous RL methods. Notably, our method exhibits strong generalization ability and produces stable results even when the number of rectangles or strip width differs from that during training.

With the acceleration of urbanization and the continuous growth of transportation demand, the traffic management of smart city road networks has become increasingly complex and critical. Traffic flow prediction, as an important component of smart transportation systems, is of great significance for optimizing traffic planning and improving traffic efficiency. The study collected and preprocessed traffic data in the smart city road network, including multi-dimensional information such as traffic flow, road conditions, and meteorological data. Then, based on the idea of graph neural networks, we constructed the topological structure of the urban road network and abstracted elements such as roads and intersections into nodes, using edges to represent their connection relationships, thus forming a graph dataset. Next, we introduced an attention mechanism to extract more representative node features through the weighted aggregation of node features, thereby achieving effective modeling of urban road network traffic flow. During the model training phase, we used real traffic datasets for experimental verification and integrated various information such as time, space, and road features into the model. The experimental results show that compared to traditional methods, this research prediction method has achieved better performance in traffic flow prediction tasks, with higher prediction accuracy and robustness. It has stronger applicability and effectiveness in different traffic scenarios. By integrating multi-dimensional information and introducing attention mechanisms, this method has significant advantages in improving the accuracy and robustness of traffic flow prediction, and has important practical significance and application prospects for the construction of smart transportation systems and the development of smart cities.