Dual Graph Research Articles

Semi-supervised multi-label feature selection based on dual dynamic graph

Traditional multi-label feature selection is usually performed under the condition of given label information, but nowadays, labeling multi-label data is a huge project, which is both time-consuming and labor-intensive, but if there is no label information condition, it will lead to poor feature selection, in order to solve this problem, this paper proposes a new semi-supervised multi-label feature selection method, i.e., semi-supervised multi-label feature selection algorithm based on dual dynamic graph. In this paper, a semi-supervised multi-label feature selection algorithm is proposed by constructing a dual dynamic graph. First, the method selects the most discriminative features for dimensionality reduction through the feature selection method of least squares regression, combined with the redundancy penalty of highly correlated features. Second, the label information is added to the construction of sample matrix similarity to learn the similarity. A semi-supervised multi-label feature selection framework is constructed by designing iterative updates of dual dynamic graphs to learn more accurate pseudo-label matrices to guide feature selection. Finally, the paper validates the above model using the alternating iteration optimization algorithm and verifies the effectiveness of the algorithm through experiments.

Point cloud registration method for indoor depth sensor acquisition system based on dual graph computation with irregular shape factors

AbstractThe registration performance determines the widespread indoor application of 3D models acquired by depth sensors. Many advanced registration methods lack comprehensive feature aggregation and poor generalization capabilities, which improves the mismatching ratio. Here, a dual graph network is proposed by incorporating irregular shape factors to make point cloud features more expressive. At first, we transform point cloud sets into the stellar graph within the local neighbourhood of each point. The deep feature and shape factor of each point are combined in the directional‐connected irregular projection space. Subsequently, the combined features are modelled as the second graph. By the attention mechanism computation, feature information is continuously aggregated with intra‐graph and inter‐graph. Finally, a loss function is utilized to confirm point correspondence and perform the registration through singular value decomposition. Extensive experiments validate that the proposed point cloud registration method achieves state‐of‐the‐art performance.

A dual contrastive learning-based graph convolutional network with syntax label enhancement for aspect-based sentiment classification

Introduction: Aspect-based sentiment classification is a fine-grained sentiment classification task. State-of-the-art approaches in this field leverage graph neural networks to integrate sentence syntax dependency. However, current methods fail to exploit the data augmentation in encoding and ignore the syntactic relation in sentiment delivery.Methods: In this work, we propose a novel graph neural network-based architecture with dual contrastive learning and syntax label enhancement. Specifically, a contrastive learning-based contextual encoder is designed, integrating sentiment information for semantics learning. Moreover, a weighted label-enhanced syntactic graph neural network is established to use both the syntactic relation and syntax dependency, which optimizes the syntactic weight between words. A syntactic triplet between words is generated. A syntax label-based contrastive learning scheme is developed to map the triplets into a unified feature space for syntactic information learning.Results: Experiments on five publicly available datasets show that our model substantially outperforms the baseline methods.Discussion: As such, the proposed method shows its effectiveness in aspect-based sentiment classification tasks.

Dual Attention Graph Convolutional Network optimized with the Sun Flower Optimization for Differential Privacy Protection Mechanism for Smart Grid against Security Attacks

A smart grid combines information and communication technology with the conventional power system to provide efficient and dependable electricity generation, transmission, distribution, and control. Utilities and users share information and electricity in a smart grid. This manuscript presents a Dual Attention Graph Convolutional Network (DAGCN) optimized with the Sun Flower Optimization Algorithm (SFO) predicting the differential privacy protection mechanism for smart grid (DPSG-DAGCN-SFO). Initially, the data is collected from NSL-KDD datasets. Afterward, the data is fed to an Adaptive Robust Cubature Kalman Filter (ARCKF) based preprocessing process. Then the preprocessed data’s are fed to Adaptive and Concise Empirical Wavelet Transform (ACEWT)for extracting features such as authentication, integrity and trusted authority. The extracted features are fed to Dual Attention Graph Convolutional Network (DAGCN)to classify the privacy protection in smart grid such as stealthy attack and no attack. The weight parameters of DAGCN are optimized using SFO. The proposed DPSG-DAGCN-SFO is implemented in python, effectiveness assessed by several performance metrics such as accuracy, error rate, precision, sensitivity, F1-score and recall. The proposed method error rate attains 2% stealthy attack and4% no attack of the of the privacy protection in smart grid. The proposed method shows better results in all existing systems like Bayesian Clustering Algorithm (BCA), Self-Adaptive Grid-Partitioning Algorithm (SGNA) and Elliptic Curve Digital Signature Algorithm (ECDSA).From the result it is concludes that the proposed DPSG-DAGCN-SFO method based error rate lower than the existing methods.

Design a Platform for Sharing Educational Resources for Student Based on Dual Attention Graph Convolutional Networks

A key component of raising educational standards is always the sharing of educational resources throughout higher education institutions. The development of digital and network infrastructure among colleges and universities can propel the sharing and exploitation of excellent educational resources to new heights. Nevertheless, there is a lack of an ideal platform for resource selection and integration in the current crop of information-based teaching tools. To overcome this drawback, the methodology used in this study was described in this research in order to classify student and teacher data more precisely. Ideological and Political Education (IPE) is the source of the data collection. The Modified Hamilton filter is used to preprocess the data during the pre-processing step. The excellent course videos, classroom performance, and interactions are all successfully classified by Dual Attention Graph Convolutional Networks (DAGCN).The neural network's weight parameter is optimized using the Lotus Effect Optimization Algorithm (LEA) to improve the DAGCN. The suggested DAGNN-LEA used with the MATLAB platform. The suggested approach was calculated using performance measures such as accuracy, precision, sensitivity, F-score, computation time, and recall. In comparison to the existing method, the suggested DAGNN-LEA method yields better results in terms of high accuracy 16.65%, 18.85%, and 17.89%, high sensitivity 16.34, 12.23%, and 18.54%, high precision 14.89%, 16.89%, and 18.23%, and low computing time 82.37%, 94.47%, and 87.76%.

English Teaching Quality Evaluation Model for Higher Education Based on Educational Data Mining Technology

Constructing teaching quality evaluation systems for higher vocational education is beneficial for schools, serving as the cornerstone for developing abilities and enabling precise education management. In this publication, a Dual Relational Graph Attention Network (ETQE-DRGAN)-Based Educational Data Mining Technology English Teaching Quality Evaluation Model for Higher Education is proposed. In three ways, this study assesses and defends the system. An overview of the system's primary components, such as the user and data administration modules, data mining, online assessment, and inquiry modules, is given in the first section. Frequent item sets are divided into two stages and the development of invalid candidate sets is limited to avoid the formation of negative association rules. The Tid-list Vertical Partitioning Query-Apriori Algorithm (TPQ-Apriori algorithm) is improved by these two approaches. The system analysis is covered in section three. In order to assess the success of instruction, this work incorporates a Dual Relational Graph Attention Networks method that takes into account several factors such as age, gender, education, teaching attitude, teaching topic, teaching technique, and teaching effectiveness. The proposed ETQE-DRGAN is implemented in MATLAB, using the School Database for calculation. Numerous performance metrics like response time, precision, accuracy, sensitivity, and F1-score are utilized to calculate the effectiveness of ETQE-DRGAN technique. The obtained outcomes indicate that the ETQE-DRGAN approach achieves the highest precision of 98.5%, Accuracy of 98% and fastest response time of 6.95swhen compared to existing methods like ETQE-AA, ETQE-DTA and ETQE-ML.

Development and Application of Big Data-Driven Intelligent Evaluation System for Physical and Aesthetic Education

Big data driven intelligence evaluation system for physical and aesthetic education are used to leverage vast amounts of data to provide more objective, nuanced, and personalized assessments of student performance and progress. This paper proposes a hybrid approach for development of big data driven intelligence evaluation system for physical and aesthetic education. The proposed hybrid approach is the combined performance of both the Dual attention graph convolution network (DAGCN) and Weibull time to event Recurrent Neural Network (WRNN).Commonly it is named as WRNN-DAGCN technique. The major objective of the proposed approach is to provide for development of big data driven intelligence evaluation system for physical and aesthetic education. WRNN is design to enhancing the efficiency of physical training. Enhance the physical training from the WRNN by using the DAGCN. By then, the MATLAB/Simulink working platform has the suggested model implemented, and the present processes are used to calculate the execution. The proposed method shows better results in all existing methods. The accuracy level of proposed BDEPA-WRNN-DAGCN approach is 98% that is higher than the other existing methods. From the result, it is conclude that the proposed approach based error rate is less compared to existing techniques.

Assessment Model Construction of International Students’ Intercultural Adaptation Based on Dual Attention Graph Convolutional Network

Assessment Model Construction of International Students’ Intercultural Adaptation Based on Dual Attention Graph Convolutional Network

Enhanced Named Entity Recognition Based on Multi-Feature Fusion Using Dual Graph Neural Networks

Enhanced Named Entity Recognition Based on Multi-Feature Fusion Using Dual Graph Neural Networks

A matrix-based method for synthesizing planetary gear trains to facilitate the creation of transmission systems

The structural synthesis of planetary gear trains (PGTs) lays an important foundation for creating new mechanisms of transmission systems. Most existing studies focused on the synthesis of single degree-of-freedom (DOF) PGTs, while the synthesis of multi-DOF PGTs is very limited. This paper presents a matrix-based method for the automatic synthesis of non-fractionated PGTs with both single and multiple DOFs. First, the concept of a modified dual graph is proposed, based on which a new and simple method is presented to efficiently detect fractionated graphs. Then, the concepts of vertex adjacency index and moment matrix are defined, and a reliable algorithm is proposed to detect isomorphic graphs. Finally, a matrix-based method for the automatic synthesis of non-fractionated PGTs is presented, and the complete databases of non-fractionated PGTs with 3–9 links and 1–3 DOFs are generated for the first time. The creative design of mechanisms of 6-speed automatic transmission (AT) is taken for instance to illustrate how to design new transmission systems based on our databases.

A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction

Graph neural networks (GNNs) have recently grown in popularity for disease prediction. Existing GNN-based methods primarily build the graph topological structure around a single modality and combine it with other modalities to acquire feature representations of acquisitions. The complicated relationship in each modality, however, may not be well highlighted due to its specificity. Further, relatively shallow networks restrict adequate extraction of high-level features, affecting disease prediction performance. Accordingly, this paper develops a new interactive deep cascade spectral graph convolutional network with multi-relational graphs (IDCGN) for disease prediction tasks. Its crucial points lie in constructing multiple relational graphs and dual cascade spectral graph convolution branches with interaction (DCSGBI). Specifically, the former designs a pairwise imaging-based edge generator and a pairwise non-imaging-based edge generator from different modalities by devising two learnable networks, which adaptively capture graph structures and provide various views of the same acquisition to aid in disease diagnosis. Again, DCSGBI is established to enrich high-level semantic information and low-level details of disease data. It devises a cascade spectral graph convolution operator for each branch and incorporates the interaction strategy between different branches into the network, successfully forming a deep model and capturing complementary information from diverse branches. In this manner, more favorable and sufficient features are learned for a reliable diagnosis. Experiments on several disease datasets reveal that IDCGN exceeds state-of-the-art models and achieves promising results.

A Dual Robust Graph Neural Network Against Graph Adversarial Attacks

Graph Neural Networks (GNNs) have gained widespread usage and achieved remarkable success in various real-world applications. Nevertheless, recent studies reveal the vulnerability of GNNs to graph adversarial attacks that fool them by modifying graph structure. This vulnerability undermines the robustness of GNNs and poses significant security and privacy risks across various applications. Hence, it is crucial to develop robust GNN models that can effectively defend against such attacks. One simple approach is to remodel the graph. However, most existing methods cannot fully preserve the similarity relationship among the original nodes while learning the node representation required for reweighting the edges. Furthermore, they lack supervision information regarding adversarial perturbations, hampering their ability to recognize adversarial edges. To address these limitations, we propose a novel Dual Robust Graph Neural Network (DualRGNN) against graph adversarial attacks. DualRGNN first incorporates a node-similarity-preserving graph refining (SPGR) module to prune and refine the graph based on the learned node representations, which contain the original nodes’ similarity relationships, weakening the poisoning of graph adversarial attacks on graph data. DualRGNN then employs an adversarial-supervised graph attention (ASGAT) network to enhance the model’s capability in identifying adversarial edges by treating these edges as supervised signals. Through extensive experiments conducted on four benchmark datasets, DualRGNN has demonstrated remarkable robustness against various graph adversarial attacks.

Behavioral Recognition of Skeletal Data Based on Targeted Dual Fusion Strategy

The deployment of multi-stream fusion strategy on behavioral recognition from skeletal data can extract complementary features from different information streams and improve the recognition accuracy, but suffers from high model complexity and a large number of parameters. Besides, existing multi-stream methods using a fixed adjacency matrix homogenizes the model’s discrimination process across diverse actions, causing reduction of the actual lift for the multi-stream model. Finally, attention mechanisms are commonly applied to the multi-dimensional features, including spatial, temporal and channel dimensions. But their attention scores are typically fused in a concatenated manner, leading to the ignorance of the interrelation between joints in complex actions. To alleviate these issues, the Front-Rear dual Fusion Graph Convolutional Network (FRF-GCN) is proposed to provide a lightweight model based on skeletal data. Targeted adjacency matrices are also designed for different front fusion streams, allowing the model to focus on actions of varying magnitudes. Simultaneously, the mechanism of Spatial-Temporal-Channel Parallel Attention (STC-P), which processes attention in parallel and places greater emphasis on useful information, is proposed to further improve model’s performance. FRF-GCN demonstrates significant competitiveness compared to the current state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120 and Kinetics-Skeleton 400 datasets. Our code is available at: https://github.com/sunbeam-kkt/FRF-GCN-master.

GLDL: Graph Label Distribution Learning

Label Distribution Learning (LDL), as a more general learning setting than generic single-label and multi-label learning, has been commonly used in computer vision and many other applications. To date, existing LDL approaches are designed and applied to data without considering the interdependence between instances. In this paper, we propose a Graph Label Distribution Learning (GLDL) framework, which explicitly models three types of relationships: instance-instance, label-label, and instance-label, to learn the label distribution for networked data. A label-label network is learned to capture label-to-label correlation, through which GLDL can accurately learn label distributions for nodes. Dual graph convolution network (GCN) Co-training with heterogeneous message passing ensures two GCNs, one focusing on instance-instance relationship and the other one targeting label-label correlation, are jointly trained such that instance-instance relationship can help induce label-label correlation and vice versa. Our theoretical study derives the error bound of GLDL. For verification, four benchmark datasets with label distributions for nodes are created using common graph benchmarks. The experiments show that considering dependency helps learn better label distributions for networked data, compared to state-of-the-art LDL baseline. In addition, GLDL not only outperforms simple GCN and graph attention networks (GAT) using distribution loss but is also superior to its variant considering label-label relationship as a static network. GLDL and its benchmarks are the first research endeavors to address LDL for graphs. Code and benchmark data are released for public access.

CMC-1 surfaces via osculating Möbius transformations between circle patterns

Given two circle patterns of the same combinatorics in the plane, the Möbius transformations mapping circumdisks of one to the other induce a P S L ( 2 , C ) PSL(2,\mathbb {C}) -valued function on the dual graph. Such a function plays the role of an osculating Möbius transformation and induces a realization of the dual graph in hyperbolic space. We characterize the realizations and obtain a one-to-one correspondence in the cases that the two circle patterns share the same discrete conformal structure. These correspondences are analogous to the Weierstrass representation for surfaces with constant mean curvature H ≡ 1 H\equiv 1 in hyperbolic space. We further establish convergence on triangular lattices.

WGDPool: A broad scope extraction for weighted graph data

Graph pooling is a commonly used operation in graph neural networks to reduce the size of graph representation. To extract key information, pooling and representation need to be coupled. We propose a graph pooling method for weighted graphs called WGDPool (Weighted Graph Dual Pooling). Unlike traditional graph representation learning methods, the weight information of edges is also fed into convolutional graph neural networks (ConvGNN) to obtain graph representations. Dual branch convolutional graph neural networks is designed to learn the nodes’ and edges’ embeddings independently, and they are fused into a comprehensive representation of graph data. Pooling, as a tool of feature extraction and scale reduction of graph representation, adopts a differentiable version of k-means clustering and a multi-item parameterized loss function. Cut loss, orthogonality loss, clustering loss, and reconstruction loss are simultaneously considered. By parameterization, WGDPool is competent for diverse graph tasks. WGDPool outperformed other graph pooling methods in such common supervised and unsupervised tasks as biological or chemical classification, bibliography clustering and integrated circuit partition, demonstrating the effectiveness of our proposed pooling method.

Independent Dual Graph Attention Convolutional Network for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) have been widely adopted in skeleton-based action recognition, achieving impressive outcomes. However, the convolution operations in GCNs fail to make full use of the original input data, which restricts its ability to accurately capture the correlation within the skeleton. To solve this issue, this study introduces an independent dual graph attention convolutional network (IDGAN). Specifically, IDGAN additionally incorporates an instinctive attention module that leverages self-attention to capture the correlation among the joints in the original input skeleton. In addition, two independent convolutional operations are used to process two self-attention modules, respectively, to further refine the relationship between skeleton joints. Extensive experiments on several publicly available datasets show that IDGAN outperforms most state-of-the-art algorithms.

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

Incorporating syntax and semantics with dual graph neural networks for aspect-level sentiment analysis

Aspect-level sentiment analysis is a more fine-grained task that aims to determine the sentiment polarity of specific aspects. Recent studies have employed graph attention networks and graph convolutional networks to model dependency trees, effectively establishing explicit associations between aspects and opinions, yielding promising performance. However, these methods have limitations in capturing complex linguistic features and the intricate dependencies between aspects and their contexts, resulting in suboptimal performance. In this paper, we propose a dual graph neural network that incorporates syntax and semantics, called IDGNN. Specifically, we utilize the relational graph attention network (RGAT) to encode the syntactic dependency tree and obtain syntactic information, while incorporating dependency labels to enhance aspect representation. Additionally, the semantic graph convolutional network (SemGCN) is employed to encode the self-attention matrix and capture semantic information, with the inclusion of orthogonal regularization to enhance semantic association. Furthermore, we introduce two fusion strategies based on gate mechanisms: the syntax fusion module (SYF) and the semantic fusion module (SEF). SYF combines contextual and syntactic representations to obtain global syntactic features, while SEF fuses semantic information with global syntactic features to obtain the final feature representation. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on several benchmark datasets.

First-order logic axiomatization of metric graph theory

The main goal of this note is to provide a First-Order Logic with Betweenness (FOLB) axiomatization of the main classes of graphs occurring in Metric Graph Theory, in analogy to Tarski's axiomatization of Euclidean geometry. We provide such an axiomatization for weakly modular graphs and their principal subclasses (median and modular graphs, bridged graphs, Helly graphs, dual polar graphs, etc), basis graphs of matroids and even Δ-matroids, partial cubes and their subclasses (ample partial cubes, tope graphs of oriented matroids and complexes of oriented matroids, bipartite Pasch and Peano graphs, cellular and hypercellular partial cubes, almost-median graphs, netlike partial cubes), and Gromov hyperbolic graphs. On the other hand, we show that some classes of graphs (including chordal, planar, Eulerian, and dismantlable graphs), closely related with Metric Graph Theory, but defined in a combinatorial or topological way, do not allow such an axiomatization.