Adaptive Graph Learning Research Articles

TAGL: Temporal-Guided Adaptive Graph Learning Network for Coordinated Movement Classification

TAGL: Temporal-Guided Adaptive Graph Learning Network for Coordinated Movement Classification

Batch process soft sensing based on data-stacking multiscale adaptive graph neural network

Abstract Soft sensing technology has found extensive application in predicting key quality variables in batch processes. However, its application in batch process is limited by the uneven batch length, the correlation of data and the difficulty in extracting the dependencies between variables and within variables. To address these issues, we propose a data-stacking multiscale adaptive graph neural network (DSMAGNN) soft sensor model. Firstly, Mutual information (MI) is used to selected quality-related variables, the 3D batch data is converted into a time-delay sequence suitable for input to the soft sensor model through the data stacking strategy, and the underlying time correlation at different time scales is preserved by incorporating the multi-scale pyramid network. Secondly, the dependencies between variables are inferred by the adaptive graph learning module, while the dependencies both within and between variables are modeled by the multi-scale temporal graph neural network. Thirdly, collaborative work across different time scales is further facilitated by the scale fusion module. Finally, the feasibility and effectiveness of the model are verified through experiments in the industrial-scale penicillin fermentation process and hot rolling process. (DSMAGNN) soft sensor model. Firstly, MI is used to selected quality-related variables, and the data stacking strategy is employed to convert 3D batch data into a time-delay sequence suitable for input to the soft sensor model. A multiscale pyramid network is utilized to preserve the underlying time dependence across different time scales. Secondly, since the dependence relationship between variables may vary across different time scales, the adaptive graph learning modules deduce scale-specific variable dependencies without relying on predefined priors. Thirdly, given the multiscale feature representation and the inferred dependency relationships between scale-specific variables, we introduce a multiscale temporal graph neural network to jointly model dependencies within and between variables. Building upon this foundation, the scale fusion module is deployed to facilitate collaboration across different temporal scales and automatically capture the significance of contributing temporal patterns. We validate the feasibility and effectiveness of the proposed model through experiments conducted in industrial-scale penicillin fermentation and hot continuous rolling processes, the results show that the proposed model is superior to several compared models, thus confirming its validity.

Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting

Multivariate time series (MTS) forecasting has attracted much attention in many intelligent applications. It is not a trivial task, as we need to consider both intra-variable dependencies and inter-variable dependencies. However, existing works are designed for specific scenarios, and require much domain knowledge and expert efforts, which is difficult to transfer between different scenarios. In this paper, we propose a scale-aware neural architecture search framework for MTS forecasting (SNAS4MTF). A multi-scale decomposition module transforms raw time series into multi-scale sub-series, which can preserve multi-scale temporal patterns. An adaptive graph learning module infers the different inter-variable dependencies under different time scales without any prior knowledge. For MTS forecasting, a search space is designed to capture both intra-variable dependencies and inter-variable dependencies at each time scale. The multi-scale decomposition, adaptive graph learning, and neural architecture search modules are jointly learned in an end-to-end framework. Extensive experiments on two real-world datasets demonstrate that SNAS4MTF achieves a promising performance compared with the state-of-the-art methods.

INCOMPLETE multi-view clustering based on low-rank adaptive graph learning

The challenge of acquiring complete data has led to substantial progress in incomplete multi-view clustering (IMVC) methods. Because graph structures can be excellent representations of data structure relationships, exceptional performance in handling incomplete data is demonstrated by graph-based methods at present. However, these methods still have their limitations. Most incomplete multi-view algorithms primarily focus on local information, neglecting global information. Therefore, these methods cannot dynamically recover the structural relationships in incomplete data by harnessing potential information from multiple perspectives and overall structural information. In response to the aforementioned concerns, we introduced an IMVC based on low-rank adaptive graph learning (IMVC-LAGL). This method initially constructs an affinity matrix based on the inter-view adjacency relationships. It also utilizes tensor low-rank constraints and consensus representation learning to explore higher-order correlations among different views. Subsequently, it adaptively reconstructs the incomplete graph structure to ultimately obtain a complete affinity relationship. It leads to excellent clustering results by integrating relevant information within views, overall structural information and potential information from multiple perspectives. We conducted experiments comparing our algorithm with eight incomplete multi-view algorithms using five different evaluation metrics. The results show that our algorithm achieves the best clustering results across eight datasets with varying missing rates. Particularly in the BBCSport dataset and YaleB dataset, the clustering accuracy of our algorithm is improved by 19.83 % and 16.41 %, respectively, compared with the second-best algorithm, under a 50 % missing rate.

A Novel Wind Power Prediction Model That Considers Multi-Scale Variable Relationships and Temporal Dependencies

Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. In this paper, a multi-scale frequency decomposition module is designed to split the raw data into high-frequency and low-frequency parts. Subsequently, features are extracted from the high-frequency information using a multi-scale temporal graph neural network combined with an adaptive graph learning module and from the low-frequency data using an improved bidirectional temporal network. Finally, the features are integrated through a cross-attention mechanism. To validate the effectiveness of the proposed model, extensive comprehensive experiments were conducted using a wind power dataset provided by the State Grid. The experimental results indicate that the MSE of the model proposed in this paper has decreased by an average of 7.1% compared to the state-of-the-art model and by 48.9% compared to the conventional model. Moreover, the improvement in model performance becomes more pronounced as the prediction horizon increases.

Multiscale information enhanced spatial-temporal graph convolutional network for multivariate traffic flow forecasting via magnifying perceptual scope

Graph Convolutional Networks (GCNs), which can model data in non-Euclidean space, have received extensive attention in multivariate traffic flow forecasting in recent years. However, most of the existing GCN forecasting methods rely on adjacency matrices defined by road network knowledge, which cannot accurately capture the inherent spatial-temporal dependencies of complex multivariate traffic flows. In this paper, a multiscale information enhanced spatial-temporal GCN is proposed, which utilizes an adaptive graph learning layer to automatically extract the dependencies between variables and constructs adaptive adjacency matrices for different datasets. In addition, an information enhancement module is introduced to magnify perceptual scope of the model and strengthen the connection between information, so that the spatial-temporal graph convolution module can better capture the spatial-temporal dependencies in the traffic flow. We conducted experiments on multivariate traffic flow data from three different real-world scenarios to evaluate the effectiveness of the proposed method. Comparative experimental results consistently show that the proposed method outperforms existing GCNs, achieving an average improvement of 21.16% on Mean Absolute Error (MAE), 18.87% on Root Mean Square Error (RMSE), and 8.27% on R2 score (R2) in the three datasets, respectively, and the results demonstrate its excellent forecasting performance in the field of multivariate traffic flow forecasting.

Stacked Network to Realize Spectral Clustering With Adaptive Graph Learning

Stacked Network to Realize Spectral Clustering With Adaptive Graph Learning

Concept factorization with adaptive graph learning on Stiefel manifold

Concept factorization with adaptive graph learning on Stiefel manifold

Discriminative Subspace Learning With Adaptive Graph Regularization

Abstract Many subspace learning methods based on low-rank representation employ the nearest neighborhood graph to preserve the local structure. However, in these methods, the nearest neighborhood graph is a binary matrix, which fails to precisely capture the similarity between distinct samples. Additionally, these methods need to manually select an appropriate number of neighbors, and they cannot adaptively update the similarity graph during projection learning. To tackle these issues, we introduce Discriminative Subspace Learning with Adaptive Graph Regularization (DSL_AGR), an innovative unsupervised subspace learning method that integrates low-rank representation, adaptive graph learning and nonnegative representation into a framework. DSL_AGR introduces a low-rank constraint to capture the global structure of the data and extract more discriminative information. Furthermore, a novel graph regularization term in DSL_AGR is guided by nonnegative representations to enhance the capability of capturing the local structure. Since closed-form solutions for the proposed method are not easily obtained, we devise an iterative optimization algorithm for its resolution. We also analyze the computational complexity and convergence of DSL_AGR. Extensive experiments on real-world datasets demonstrate that the proposed method achieves competitive performance compared with other state-of-the-art methods.

Aligned multi-view clustering for unmapped data via weighted tensor nuclear norm and adaptive graph learning

Multi-view clustering (MVC) is a prevailing clustering method that exploits consistency and complementarity among views to achieve satisfactory performance. The premise of MVC is that the samples of each view are mapped, that is, the same sample is placed in the same position among different views. However, multi-view data collected from the real world would also be unmapped, and thus conventional MVC methods are facing challenges. To address this problem, in our paper, we propose a novel aligned multi-view clustering (A-MVC) method for unmapped data. Specifically, the Calinski–Harabasz index is first employed to find the optimal view, and then the samples in other views are aligned to the samples in the optimal view with the help of the alignment matrix so that the unmapped data are converted into mapped ones. In addition, A-MVC absorbs the advantages of conventional multi-view clustering and employs both weighted tensor nuclear norm (TNN) and graph learning algorithms, which simultaneously learn the global representation and capture the local information. Finally, we integrate the alignment algorithm with a low-rank constraint based on weighted TNN and graph constraints into a unified framework. Extensive experimental results on certain unmapped multi-view datasets show that A-MVC outperforms other state-of-the-art methods. The codes and datasets are available at https://github.com/bingly/A-MVC.

Cascaded Adaptive Graph Representation Learning for Image Copy-Move Forgery Detection

In the realm of image security, there has been a burgeoning interest in harnessing deep learning techniques for the detection of digital image copy-move forgeries, resulting in promising outcomes. The generation process of such forgeries results in a distinctive topological structure among patches, and collaborative modeling based on these underlying topologies proves instrumental in enhancing the discrimination of ambiguous pixels. Despite the attention received, existing deep learning models predominantly rely on convolutional neural networks (CNNs), falling short in adequately capturing correlations among distant patches. This limitation impedes the seamless propagation of information and collaborative learning across related patches. To address this gap, our work introduces an innovative framework for image copy-move forensics rooted in graph representation learning. Initially, we introduce an adaptive graph learning approach to foster collaboration among related patches, dynamically learning the inherent topology of patches. The devised approach excels in promoting efficient information flow among related patches, encompassing both short-range and long-range correlations. Additionally, we formulate a cascaded graph learning framework, progressively refining patch representations and disseminating information to broader correlated patches based on their updated topologies. Finally, we propose a hierarchical cross-attention mechanism facilitating the exchange of information between the cascaded graph learning branch and a dedicated forgery detection branch. This equips our method with the capability to jointly grasp the homology of copy-move correspondences and identify inconsistencies between the target region and the background. Comprehensive experimental results validate the superiority of our proposed scheme, providing a robust solution to security challenges posed by digital image manipulations.

A Domain Adaptive Graph Learning Framework to Early Detection of Emergent Healthcare Misinformation on Social Media

A Domain Adaptive Graph Learning Framework to Early Detection of Emergent Healthcare Misinformation on Social Media

Omics data classification using constitutive artificial neural network optimized with single candidate optimizer

ABSTRACT Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics’. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using Adaptive variational Bayesian filtering (AVBF) to replace missing values. The pre-processing data is fed to Zebra Optimization Algorithm (ZOA) for dimensionality reduction. Then, the Constitutive Artificial Neural Network (CANN) is employed to classify omics data. The weight parameter is optimized by Single Candidate Optimizer (SCO). The proposed ODC-ZOA-CANN-SCO method attains 25.36%, 21.04%, 22.18%, 26.90%, and 28.12% higher accuracy when analysed to the existing methods like multi-omics data integration utilizing adaptive graph learning and attention mode for patient categorization with biomarker identification (MOD-AGL-AM-PABI), deep learning method depending upon multi-omics data integration to create risk stratification prediction mode for skin cutaneous melanoma (DL-MODI-RSP-SCM), Deep belief network-base model for identifying Alzheimer’s disease utilizing multi-omics data (DDN-DAD-MOD), hybrid cancer prediction depending upon multi-omics data and reinforcement learning state action reward state action (HCP-MOD-RL-SARSA), machine learning basis method under omics data including biological knowledge database for cancer clinical endpoint prediction (ML-ODBKD-CCEP) methods, respectively.

Adaptive multi-view subspace learning based on distributed optimization

As the rapid development of Internet of Things (IoT), information is collected from different sensors and stored in distributed devices which can be regarded as the multi-view data. There are currently numerous clustering algorithms designed to handle multi-view data. However, most of these algorithms still suffer from the following problems: They are designed to operate directly on raw data, which preserves excessive redundant information. They primarily focus on pairwise relationships between views, neglecting the intricate high-order connections among multiple views. The prior information of singular values is not taken into account in multiple views and different views are considered to have equal contributions for clustering. To efficiently address above problems, adaptive multi-view subspace learning based on distributed optimization (AMSLDO) is proposed in this paper. Specifically, the original multi-view data is projected to a low-dimensional space for subspace representation, and multiple representation matrices are stacked in a tensor with weighted tensor nuclear norm to obtain high-order correlations and discover the prior information of singular values. Furthermore, the consensus matrix is learned from different representation matrices through adaptive weights. Meanwhile, samples are partitioned into the ideal number of clusters through Laplacian rank constraint. An efficient distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) framework is designed to solve the proposed model. Extensive experiments are conducted on six datasets, demonstrating the superiority of the proposed model compared with eleven state-of-the-art methods.

Adaptive orthogonal semi-supervised feature selection with reliable label matrix learning

Feature selection (FS) can select features with high value from high-dimensional data as much as possible and reduce the dimensionality of the data to improve the performance of the machine learning model and enhance the generalization ability. In semi-supervised FS (SSFS), regression-based methods utilize the label matrix, and the quality of the label matrix can directly affect the performance of FS. Hence, the importance of the reliable label matrix is obvious. Orthogonal regression (OR) can retain more information in the subspace than least squares regression (LSR). Therefore, this paper introduces OR into SSFS, and then the label scaling technique is used to learn a reliable label matrix. Also, this study utilizes adaptive graph learning to exploit more structural information about the data. Two constraints, Frobenius-norm or maximum information entropy imposed on the similarity matrix and two adaptive orthogonal SSFS (AGLSOFS) methods with reliable label matrix learning are constructed. The impact of these two constraints on the construction of dynamic similarity graphs and FS results is discussed. Effective optimization algorithms for these two methods are based on the Alternating Direction Method of Multipliers (ADMM) and Generalized Power Iteration (GPI). Experiments are conducted on 15 benchmark datasets, and the results show that: (1) similarity graphs constructed using both original and projected data are more accurate; (2) both constraints are valid; (3) both of the methods of this paper perform well on most datasets; (4) OR performs better than LSR for FS; and (5) the scaling factor affects the convergence speed of the model.

Adaptive Graph Learning for Multimodal Conversational Emotion Detection

Multimodal Emotion Recognition in Conversations (ERC) aims to identify the emotions conveyed by each utterance in a conversational video. Current efforts encounter challenges in balancing intra- and inter-speaker context dependencies when tackling intra-modal interactions. This balance is vital as it encompasses modeling self-dependency (emotional inertia) where speakers' own emotions affect them and modeling interpersonal dependencies (empathy) where counterparts' emotions influence a speaker. Furthermore, challenges arise in addressing cross-modal interactions that involve content with conflicting emotions across different modalities. To address this issue, we introduce an adaptive interactive graph network (IGN) called AdaIGN that employs the Gumbel Softmax trick to adaptively select nodes and edges, enhancing intra- and cross-modal interactions. Unlike undirected graphs, we use a directed IGN to prevent future utterances from impacting the current one. Next, we propose Node- and Edge-level Selection Policies (NESP) to guide node and edge selection, along with a Graph-Level Selection Policy (GSP) to integrate the utterance representation from original IGN and NESP-enhanced IGN. Moreover, we design a task-specific loss function that prioritizes text modality and intra-speaker context selection. To reduce computational complexity, we use pre-defined pseudo labels through self-supervised methods to mask unnecessary utterance nodes for selection. Experimental results show that AdaIGN outperforms state-of-the-art methods on two popular datasets. Our code will be available at https://github.com/TuGengs/AdaIGN.

Data-unbalanced traffic accident prediction via adaptive graph and self-supervised learning

Traffic accident prediction is an important research problem, which can help to identify dangerous situations on the road in advance and take appropriate measures. Nonetheless, real-world traffic accident data suffers from a significant data unbalance problem, as accident occurrences vary unevenly in both spatial and data domains. This unbalance can easily lead to the prediction methods biased towards the side with more data. Recently, researchers have proposed a series of effective prediction methods based on deep learning and graph theory. Existing graph-based methods always adopt the predefined distance graph. However, these methods cannot fully capture the spatial correlations among regions that are far away from each other but share similar accident patterns. To address these challenges, we propose a traffic accident prediction method that combines Adaptive Graphs with Self-Supervised Learning (AGSSL). In the proposed method, we can adaptively construct graph structures to learn global spatial correlations among urban regions. Meanwhile, two self-supervised learning modules called Graph Infomax and Focal Contrastive Regularization are used to learn a robust representation of traffic accidents data under an unbalanced distribution. Experiment results show that AGSSL outperforms SOTA methods in traffic accident prediction.

Dense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation.

In this paper, we propose a dense multi-scale adaptive graph convolutional network (DMA-GCN) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously. The local learning task aggregates spatial contextual information from aligned spatial neighborhoods of nodes, at multiple scales, while global learning explores pairwise affinities between nodes, located globally at different positions in the image. We propose two different structures of building models, whereby the local and global convolutional units are combined by following an alternating or a sequential manner. Secondly, based on the previous models, we develop the DMA-GCN network, by utilizing a densely connected architecture with residual skip connections. This is a deeper GCN structure, expanded over different block layers, thus being capable of providing more expressive node feature representations. Third, all units pertaining to the overall network are equipped with their individual adaptive graph learning mechanism, which allows the graph structures to be automatically learned during training. The proposed cartilage segmentation method is evaluated on the entire publicly available Osteoarthritis Initiative (OAI) cohort. To this end, we have devised a thorough experimental setup, with the goal of investigating the effect of several factors of our approach on the classification rates. Furthermore, we present exhaustive comparative results, considering traditional existing methods, six deep learning segmentation methods, and seven graph-based convolution methods, including the currently most representative models from this field. The obtained results demonstrate that the DMA-GCN outperforms all competing methods across all evaluation measures, providing DSC=95.71% and DSC=94.02% for the segmentation of femoral and tibial cartilage, respectively.

Dual auto-weighted multi-view clustering via autoencoder-like nonnegative matrix factorization

Multi-view clustering (MVC) can exploit the complementary information among multi-view data to achieve the satisfactory performance, thus having extensive potentials for practical applications. Although Nonnegative Matrix Factorization (NMF) has emerged as an effective technique for MVC, the existing NMF-based methods still have two main limitations: 1) They solely focus on the reconstruction of original data, which can be regarded as the decoder of an autoencoder, while neglecting the low-dimensional representation learning. 2) They lack the ability to effectively capture both linear and nonlinear structures of data. To solve these problems, in this paper, we propose a Dual Auto-weighted multi-view clustering model based on Autoencoder-like NMF (DA2NMF), which enables a comprehensive exploration of both linear and nonlinear structures. Specifically, we establish an autoencoder-like NMF model that learns linear low-dimensional representations by integrating data reconstruction and representation learning within a unified framework. Moreover, the adaptive graph learning is introduced to explore the nonlinear structures in data. We further design a dual auto-weighted strategy to adaptively compute weights for different views and low-dimensional representations, thereby obtaining an enhanced consistent graph. An effective algorithm based on Multiplicative Update Rule (MUR) is developed to solve the DA2NMF with the theoretical convergence guarantee. Experimental results show that the proposed DA2NMF can effectively improve the clustering performance compared with the state-of-the-art MVC algorithms.

Multi-target feature selection with subspace learning and manifold regularization

Existing supervised Multi-Target Feature Selection (MTFS) methods seldom consider the nearest-neighbor relationship and statistical correlation of samples underlying the output space, which leads the result of feature selection to be easily interfered by the output noise, thus making it difficult to achieve satisfactory performance. This paper proposes a novel MTFS method to preserve both the global and local target correlations. Specifically, the low-rank constraint is introduced to achieve multi-layer regression structure to better decouple the inter-input and inter-target relationships. Moreover, the local nearest-neighbor relationships and variable correlations of the sample points in the output space are also explored through adaptive graph and manifold learning, to better utilize the target correlations to improve the MTFS performance. Following the above principle, the resulting objective function and the corresponding optimization algorithm are proposed. Extensive experiments on several public datasets show that the proposed method is superior to other state-of-the-art methods.