An interactive address matching method based on a graph attention mechanism

Exploiting heterogeneous information networks (HIN) to top-N recommendation has been shown to alleviate the data sparsity problem present in recommendation systems. This requires careful effort in extracting relevant knowledge from HIN. However, existing models in this setting have the following shortcomings. Mainly, they are not end-to-end, which puts the burden on the system to first learn similarity or commuting matrix offline using some manually selected meta-paths before we train for the top-N recommendation objective. Further, they do not attentively extract user-specific information from HIN, which is essential for personalization. To address these challenges, we propose an end-to-end neural network model – GAMMA (Graph and Multi-view Memory Attention mechanism). We aim to replace the offline meta-path based similarity or commuting matrix computation with a graph attention mechanism. Besides, with different semantics of items in HIN, we propose a multi-view memory attention mechanism to learn more profound user-specific item views. Experiments on three real-world datasets demonstrate the effectiveness of our model for top-N recommendation setting.

In e-learning, the rapid expansion of learning resources poses challenges for learners in finding suitable materials due to their diverse preferences and cognitive abilities. Consequently, personalized learning path recommendation has emerged as a pivotal research area, especially for advancing e-learning systems. This paper introduces an algorithmic framework that integrates deep reinforcement learning with a graph attention mechanism to tailor learning paths to individual learners. The online course dataset is selected and a series of controlled experiments are conducted on the common recommendation models proposed in the past, and the experimental results are analyzed using a combination of two evaluation indices, such as data evaluation results and model variance. The experimental results show that adding the attention mechanism can significantly improve the accuracy of the model recommendation, compared with the deep reinforcement learning model without adding the graph attention mechanism, the comprehensive scores of the students in the test set were improved by 5.8 and 12.8 points, respectively, and the accuracy was improved by 5.3% compared with the previous deep learning model; the deep reinforcement model used in this paper with the addition of the labeling feedback mechanism was improved by 5.3% compared with the deep learning with feedback mechanism. In the recommendation model, the final scores of the students were improved by 3.7 and 8.2 points, respectively. In addition, the Advanced test set in the recommendation model of the learning path recommended by the student score improvement is more than two times of the Middle test set’s scores improvement, indicating that more learning recommended object knowledge points the richer, the model recommendation accuracy rate is higher. By merging graph attention mechanisms with deep reinforcement learning, our system provides precise recommendations, offering insights into the development of efficient personalized learning path systems and accelerating their educational applications.

Novel blockchain transaction provenance model with graph attention mechanism

Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make their next judgments according to the behavior of the ambient vehicles. Thus, vehicles need to consider temporal and spatial interactions to reduce the risk of future collisions. In the current work, a trajectory prediction method is put forward in accordance with a graph attention mechanism. We add the absolute and relative motion information of vehicles to the input of the model to describe the vehicles’ past motion states more accurately. LSTM models are employed to process the historical motion information of vehicles, as well as the temporal correlations in interactions. The graph attention mechanism is applied to capture the spatial correlations between vehicles. Utilizing a decoder rooted in an LSTM framework, the future trajectory distribution is generated. Evaluation on the NGSIM US-101 and I-80 datasets substantiates the superiority of our approach over existing state-of-the-art algorithms. Moreover, the predictions of our model are analyzed.

The constraints of road network topology and dynamically changing traffic states over time make the task of traffic flow prediction extremely challenging. Most existing methods use CNNs or GCNs to capture spatial correlation. However, convolution operator-based methods are far from optimal in their ability to fuse node features and topology to adequately model spatial correlation. In order to model the spatio-temporal features of traffic flow more effectively, this paper proposes a traffic flow prediction model, the Spatio-Temporal Graph Attention Network (STGAN), which is based on graph attention mechanisms and residually connected gated recurrent units. Specifically, a graph attention mechanism and a random wandering mechanism are used to extract spatial features of the traffic network, and gated recurrent units with residual connections are used to extract temporal features. Experimental results on real-world public transportation datasets show that our approach not only yields state-of-the-art performance, but also exhibits competitive computational efficiency and improves the accuracy of traffic flow prediction.

in recent years, graph neural network has been widely used. Attention mechanism is introduced into the graph neural network to make it more applicable. Both GAT and AGNN prove that attention mechanism plays an important role in graph neural network. Attention mechanism algorithms such as gat and AGNN directly use a self-learning variable to do the point product after calculating the connection (or similarity calculation) of node and neighbor features (without further processing of the calculation results). Finally, we get an aggregation of neighbor information. A cosine similarity distance pruning algorithm based on graph attention mechanism (CDP-GA) is proposed to optimize the attention matrix of nodes and their adjacent nodes. By calculating the cosine similarity between node features and neighbor features (the feature here is obtained by linear transformation), the similarity of nodes is regarded as the distance between nodes (or the weight of edges). And we think that the aggregation degree of node information is inversely proportional to the distance between nodes (similar to the heat conduction formula). In the method, we prune the neighborhood of the node according to the cosine similarity to get the final attention coefficient matrix. In this way, the attention mechanism in the graph neural network is further refined, and the loss of aggregation neighbor information is reduced. In the experiments of three datasets, our model is compared with the experimental classification of GAT and AGNN and the experiment of correlation graph neural network algorithm. Finally, it is proved that the algorithm is better than three known datasets.

Drug sensitivity is essential for identifying effective treatments. Meanwhile, circular RNA (circRNA) has potential in disease research and therapy. Uncovering the associations between circRNAs and cellular drug sensitivity is crucial for understanding drug response and resistance mechanisms. In this study, we proposed DeepHeteroCDA, a novel circRNA-drug sensitivity association prediction method based on multi-scale heterogeneous network and graph attention mechanism. We first constructed a heterogeneous graph based on drug-drug similarity, circRNA-circRNA similarity, and known circRNA-drug sensitivity associations. Then, we embedded the 2D structure of drugs into the circRNA-drug sensitivity heterogeneous graph and use graph convolutional networks (GCN) to extract fine-grained embeddings of drug. Finally, by simultaneously updating graph attention network for processing heterogeneous networks and GCN for processing drug structures, we constructed a multi-scale heterogeneous network and use a fully connected layer to predict the circRNA-drug sensitivity associations. Extensive experimental results highlight the superior of DeepHeteroCDA. The visualization experiment shows that DeepHeteroCDA can effectively extract the association information. The case studies demonstrated the effectiveness of our model in identifying potential circRNA-drug sensitivity associations. The source code and dataset are available at https://github.com/Hhhzj-7/DeepHeteroCDA.

Fault detection of complicated processes based on an enhanced transformer network with graph attention mechanism

Optimal join order selection often leads to optimal query execution plans while traversing the solution space is almost impossible which expands rapidly with the increase of join complexity. In the past research, considerable attention has been paid to heuristic rules for pruning. However, that may not work well facing complex SQL queries. Recent use of the reinforcement learning avoids traversing the solution space, but inaccurate long-term rewards may be obtained because of the inconsistent influence of various parts in the join tree. In this paper, SOAR is proposed, a novel learned optimizer that selects join orders through reinforcement learning with graph attention network. SOAR captures the tree structure by inputting the join tree in which each node carries database features into graph neural network and learns the inconsistent influence of join tree on long-term reward with graph attention mechanism in the process of reinforcement learning. The preliminary results demonstrate that SOAR can match or outperform the optimizer in PostgreSQL.

Extracting and representing text features is the most important part of text classification. Aiming at the problem of incomplete feature extraction in traditional text classification methods, a text classification model based on graph convolution neural network and attention mechanism is proposed. Firstly, the text is input into BERT (Bi-directional Encoder Representations from Transformers) model to obtain the word vector representation, the context semantic information of the given text is learned by the BiGRU (Bi-directional Gated Recurrent Unit), and the important information is screened by attention mechanism and used as node features. Secondly, the dependency syntax diagram and the corresponding adjacency matrix of the input text are constructed. Thirdly, the GCN (Graph Convolution Neural Network) is used to learn the node features and adjacency matrix. Finally, the obtained text features are input into the classifier for text classification. Experiments on two datasets show that the proposed model achieves a good classification effect, and better accuracy is achieved in comparison with baseline models.

A novel quality prediction model based on dual-layer graph supervised embedding with multi-granularity attention enhancement mechanisms.

Peptides have recently emerged as promising therapeutic agents against various diseases. For both research and safety regulation purposes, it is of high importance to develop computational methods to accurately predict the potential toxicity of peptides within the vast number of candidate peptides. In this study, we proposed ATSE, a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural networks and attention mechanism. More specifically, it consists of four modules: (i) a sequence processing module for converting peptide sequences to molecular graphs and evolutionary profiles, (ii) a feature extraction module designed to learn discriminative features from graph structural information and evolutionary information, (iii) an attention module employed to optimize the features and (iv) an output module determining a peptide as toxic or non-toxic, using optimized features from the attention module. Comparative studies demonstrate that the proposed ATSE significantly outperforms all other competing methods. We found that structural information is complementary to the evolutionary information, effectively improving the predictive performance. Importantly, the data-driven features learned by ATSE can be interpreted and visualized, providing additional information for further analysis. Moreover, we present a user-friendly online computational platform that implements the proposed ATSE, which is now available at http://server.malab.cn/ATSE. We expect that it can be a powerful and useful tool for researchers of interest.

Technological progress has driven the vigorous development of multimedia technology, with massive amounts of multimedia data generated every moment. Efficient sentiment analysis algorithms can help people understand and use multimedia data, reduce production and management costs, and improve the efficiency of human-computer interaction. The extraction of emotional features from multimedia information is a crucial step in capturing semantic information. Accurately extracting emotional states from multimedia content has become one of the important focuses of information processing. Traditional methods for extracting emotional features have limited accuracy in information disclosure due to their singularity, resulting in a significant gap between information content and actual cognition. To address this issue, a multimedia emotion representation method combining graph convolutional adversarial learning and attention mechanism was proposed. This method achieved the final multimedia emotion design model by constructing an emotion representation feature model, adversarial design of multidimensional emotion labels, and attention modules for local and overall emotions. The proposed hybrid model was tested and analyzed, and the results showed that the average loss value of the multimedia emotion fusion algorithm was less than 0.3, and its accurate recognition rate of video data reached 90.47%. The recognition accuracy of neutral, angry, happy, and sad emotional labels exceeded 85%, with the highest value reaching 92.30%, significantly better than other algorithms. In addition, the improved hybrid algorithm performed better in information representation and extraction capabilities, with an increase in emotional information interactivity of over 40% and an overall average time consumption of less than 1.5 s. The study analyzes multimedia emotional data from two dimensions: features and labels, effectively providing important research value and significance for emotional data mining and emotional content capture.

In recent years, research has focused heavily on Knowledge Tracing (KT), a crucial technique for learner state modeling in intelligent education. There are several KT models based on graph convolutional networks (GCN-KTs), but none of them can distinguish the importance of exercises or knowledge concepts. Existing GCN-KTs treat all neighboring nodes “equally” when performing graph convolution operations for exercise and concept embeddings, resulting in insufficient robustness of the generated node representations. Based on GCN-KTs, we offer a Knowledge Tracing model that is based on the Graph Attention Mechanism (GAFKT) and has an encoder-decoder structure. The encoder applies the self-attention layer to the topology and node features of the exercises and concepts, respectively, and the decoder uses the inner product to reconstruct the graph structure. In GAFKT, the semantic model of student knowledge and exercises is further enriched by mapping the external feature embeddings in the original data to the embeddings of the exercises and concepts in the same space. This work was experimented with several the most advanced models on two open source datasets for comparison, and the results demonstrated the effectiveness of GAFKT.

Anomaly detection on multivariate Key Performance Indicators (KPIs) is a key procedure for the quality and reliability of large-scale Cyber-Physical Systems (CPSs). Although extensive efforts have been paid in learning normal data distributions, the spatial dependence of different dimensional KPIs is barely explored to reasonably represent the complexity and time-varying nature of systems. In this paper, we propose to model the spatial dependence of multivariate KPI by combining a more reasonable graph learning method with a graph attention mechanism to obtain the complex spatial dependence in an unsupervised manner. First, we transform the multivariate KPI into graph structures with a specially designed KPI graph learning module. Second, the Graph Attention mechanism extracts the spatial dependence in the KPI graphs. Finally, our method jointly trains forecasting-based model and reconstruction-based model to detect anomalies. Through a large number of related experiments on four real-world datasets, we demonstrate the feasibility of our method and the F1-Score improves by 9% over the baseline model. Further analysis shows that the graph learning method in this paper can more reasonably describe the dependence between multivariate KPI, and the graph attention mechanism can more accurately capture the correlation between them, which is helpful for fault diagnosis.