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The accurate diagnosis of autism spectrum disorder (ASD) is of great practical significance in clinical practice. The spontaneous hemodynamic fluctuations were collected by functional near-infrared spectroscopy (fNIRS) from the bilateral frontal and temporal cortices of typically developing (TD) children and children with ASD. Since traditional machine learning and deep learning methods cannot make full use of the potential spatial dependence between variable pairs, and require a long time series to diagnose ASD. Therefore, we use adaptive spatiotemporal graph convolution network (ASGCN) and short time series to classify ASD and TD. To capture spatial and temporal features of fNIRS multivariable time series without the pre-defined graph, we combined the improved adaptive graph convolution network (GCN) and gated recurrent units (GRU). We conducted a series of experiments on the fNIRS dataset, and found that only using 2.1 s short time series could achieve high precision classification, with an accuracy of 95.4%. This suggests that our approach may have the potential to detect pathological signals in autism patients within 2.1 s. In different brain regions, the left frontal lobe has the best classification effect, and the abnormalities occur more frequently in left hemisphere and frontal lobe region. Moreover, we also found that there were correlations between multiple channels, which had different degrees of influence on the classification of ASD. From this, we can also generalize to a wider range, there may be potential correlations between different brain regions. This may help to better understand the cortical mechanism of ASD.

Small-signal stability (SSA) is important to power system security. A data-driven approach is established for rapid prediction of the power system oscillation characteristics. The key of the approach is the Graph Convolution Networks (GCN) with residual mechanism, which works to aggregate features from high-dimension steady-state operation information and is denoted as ResGCN (RESidual GCN) in the paper. The residual mechanism helps to overcome the network degradation phenomenon. Both the oscillation frequency and damping ratio of multiple modes can be predicted by the proposed model. The performance of the proposed scheme as well as its adaptability to the power system topological changes is verified on the IEEE 39 Bus system.

Recommendation Information Systems (RIS) are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet. Graph Convolution Network (GCN) algorithms have been employed to implement the RIS efficiently. However, the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process. To address this issue, we propose a Weighted Forwarding method using the GCN (WF-GCN) algorithm. The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning. By applying the WF-GCN algorithm, which adjusts weights for each hop layer before forwarding to the next, nodes with many neighbors achieve higher embedding values. This approach facilitates the learning of more hop layers within the GCN framework. The efficacy of the WF-GCN was demonstrated through its application to various datasets. In the MovieLens dataset, the implementation of WF-GCN in LightGCN resulted in significant performance improvements, with recall and NDCG increasing by up to +163.64% and +132.04%, respectively. Similarly, in the Last.FM dataset, LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements, with the recall and NDCG metrics rising by up to +174.40% and +169.95%, respectively. Furthermore, the application of WF-GCN to Self-supervised Graph Learning (SGL) and Simple Graph Contrastive Learning (SimGCL) also demonstrated notable enhancements in both recall and NDCG across these datasets.

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

The critical task of recognizing human–object interactions (HOI) finds its application in the domains of surveillance, security, healthcare, assisted living, rehabilitation, sports, and online learning. This has led to the development of various HOI recognition systems in the recent past. Thus, the purpose of this study is to develop a novel graph-based solution for this purpose. In particular, the proposed system takes sequential data as input and recognizes the HOI interaction being performed in it. That is, first of all, the system pre-processes the input data by adjusting the contrast and smoothing the incoming image frames. Then, it locates the human and object through image segmentation. Based on this, 12 key body parts are identified from the extracted human silhouette through a graph-based image skeletonization technique called image foresting transform (IFT). Then, three types of features are extracted: full-body feature, point-based features, and scene features. The next step involves optimizing the different features using isometric mapping (ISOMAP). Lastly, the optimized feature vector is fed to a graph convolution network (GCN) which performs the HOI classification. The performance of the proposed system was validated using three benchmark datasets, namely, Olympic Sports, MSR Daily Activity 3D, and D3D-HOI. The results showed that this model outperforms the existing state-of-the-art models by achieving a mean accuracy of 94.1% with the Olympic Sports, 93.2% with the MSR Daily Activity 3D, and 89.6% with the D3D-HOI datasets.

Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can learn from such inputs are essential for working with graph data effectively. This paper identifies nodes and edges within specific applications, such as text, entities, and relations, to create graph structures. Different applications may require various graph neural network (GNN) models. GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges. The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today. It also discusses the message-passing mechanism employed by GNN models and examines the strengths and limitations of these models in different domains. Furthermore, the paper explores the diverse applications of GNNs, the datasets commonly used with them, and the Python libraries that support GNN models. It offers an extensive overview of the landscape of GNN research and its practical implementations.

[Purpose/Significance] Literature classification is a fundamental task in library and information service, which is of great value for information resource management, and literature retrieval and acquisition. Deep learning-based literature classification methods are the current mainstream methods in text classification, which employ neural networks to model and use the textual content for literature classification. This approach only utilizes the information of the literature itself, but ignores the knowledge of the association between the literature. By observing the data, we found that literature in the same category tends to share more keyword information. The literature can build association networks through keywords to form structural relationships between literature. We attempt to utilize this structural in-formation to improve the performance of literature classification. [Methods/Process] This paper proposes a method that can model the structural representation of the literature and employ this representation to enhance traditional literature classification methods. Specifi-cally, we first constructed a large-scale keyword dictionary based on the collected data from about 930,000 documents. Second, we extracted the keyword set from the titles and abstracts of papers by a two-way maximum matching algorithm and constructed the keyword-literature graph data with the literature and keywords as nodes and the inclusion relationship between the documents and keywords as edges. The literature was connected with each other by keywords. Furthermore, we employed graph convolutional neural network to model the literature graph and learn the representation of literature and keywords in the keyword-literature graph. The literature representation generated by graph neural network contained the structural relationships between the literature. In addition, we employed Bert+BiLSTM to model the textual content representation of literature. Finally, the structural and textual representations of the literature were concatenated, and the classification of the literature was performed based on this representation. [Results/Conclusions] We constructed a literature classification dataset containing 423 classes and divided the training set, validation set and test set according to the ratio of 8:1:1. We conducted literature classification experiments on this dataset. The experimental results show that the structural information of literature can effectively enhance the performance of traditional literature classification methods. The results of the stripping experiments also show that the structural information alone is insufficient for the literature classification task. Through detailed analysis of the error data, we found that the model still has problems in handling some less frequent keywords and concepts. In the future, we plan to use small-sample learning methods to solve the classification problem for literature categories with less data.

Dynamic network link prediction is becoming a hot topic in network science, due to its wide applications in biology, sociology, economy and industry. However, it is a challenge since network structure evolves with time, making long-term prediction of adding/deleting links especially difficult. Inspired by the great success of deep learning frameworks, especially the convolution neural network (CNN) and long short-term memory (LSTM) network, we propose a novel end-to-end model with a Graph Convolution Network(GCN) embedded LSTM, named GC-LSTM, for dynamic network link prediction. Thereinto, LSTM is adopted as the main framework to learn the temporal features of all snapshots of a dynamic network. While for each snapshot, GCN is applied to capture the local structural properties of nodes as well as the relationship between them. One benefit is that our GC-LSTM can predict both added and removed links, making it more practical in reality, while most existing dynamic link prediction methods can only handle removed links. Extensive experiments demonstrated that GC-LSTM achieves outstanding performance and outperforms existing state-of-the-art methods.

Background The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases. Results In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs. Conclusion We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.

Real-world problems often exhibit complex relationships and dependencies, which can be effectively captured by graph learning systems. Graph attention networks (GATs) have emerged as a powerful and versatile framework in this direction, inspiring numerous extensions and applications in several areas. In this review, we present a thorough examination of GATs, covering both diverse approaches and a wide range of applications. We examine the principal GAT-based categories, including Global Attention Networks, Multi-Layer Architectures, graph-embedding techniques, Spatial Approaches, and Variational Models. Furthermore, we delve into the diverse applications of GATs in various systems such as recommendation systems, image analysis, medical domain, sentiment analysis, and anomaly detection. This review seeks to act as a navigational reference for researchers and practitioners aiming to emphasize the capabilities and prospects of GATs.