Entropy-aware self-training for graph convolutional networks

Graph convolutional networks (GCNs) are widely believed to perform well in the graph node classification task, and homophily assumption plays a core rule in the design of previous GCNs. However, some recent advances on this area have pointed out that homophily may not be a necessity for GCNs. For deeper analysis of the critical factor affecting the performance of GCNs, we first propose a metric, namely, neighborhood class consistency (NCC), to quantitatively characterize the neighborhood patterns of graph datasets. Experiments surprisingly illustrate that our NCC is a better indicator, in comparison to the widely used homophily metrics, to estimate GCN performance for node classification. Furthermore, we propose a topology augmentation graph convolutional network (TA-GCN) framework under the guidance of the NCC metric, which simultaneously learns an augmented graph topology with higher NCC score and a node classifier based on the augmented graph topology. Extensive experiments on six public benchmarks clearly show that the proposed TA-GCN derives ideal topology with higher NCC score given the original graph topology and raw features, and it achieves excellent performance for semi-supervised node classification in comparison to several state-of-the-art (SOTA) baseline algorithms.

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks. Additionally, due to the capability to handle long-term dependencies, the long-term memory is used to predict the time series of traffic flow and extract the time features. The proposed model is evaluated using real-world data, which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China. The results show that the developed combined GCN-LSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model, and the combined ARIMA-LSTM model in traffic flow has a strong spatiotemporal correlation.

Exploiting node-feature bipartite graph in graph convolutional networks

Skeleton-based action recognition aims to project skeleton sequences to action categories, where skeleton sequences are derived from multiple forms of pre-detected points. Compared with earlier methods that focus on exploring single-form skeletons via Graph Convolutional Networks (GCNs), existing methods tend to improve GCNs by leveraging multi-form skeletons due to their complementary cues. However, these methods (either adapting structure of GCNs or model ensemble) require the co-existence of all forms of skeletons during both training and inference stages, while a typical situation in real life is the existence of only partial forms for inference. To tackle this issue, we present Adaptive Cross-Form Learning (ACFL), which empowers well-designed GCNs to generate complementary representation from single-form skeletons without changing model capacity. Specifically, each GCN model in ACFL not only learns action representation from the single-form skeletons, but also adaptively mimics useful representations derived from other forms of skeletons. In this way, each GCN can learn how to strengthen what has been learned, thus exploiting model potential and facilitating action recognition as well. Extensive experiments conducted on three challenging benchmarks, i.e., NTU-RGB+D 120, NTU-RGB+D 60 and UAV-Human, demonstrate the effectiveness and generalizability of the proposed method. Specifically, the ACFL significantly improves various GCN models (i.e., CTR-GCN, MS-G3D, and Shift-GCN), achieving a new record for skeleton-based action recognition.

Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity and mortality, where early detection benefits the population. However, the early diagnosis rate of COPD is low due to the absence or slight early symptoms. In this paper, a novel method based on graph convolution network (GCN) for early detection of COPD is proposed, which uses small and weakly labeled chest computed tomography image data from the publicly available Danish Lung Cancer Screening Trial database. The key idea is to construct a graph using regions of interest randomly selected from the segmented lung parenchyma and then input it into the GCN model for COPD detection. In this way, the model can not only extract the feature information of each region of interest but also the topological structure information between regions of interest, that is, graph structure information. The proposed GCN model achieves an acceptable performance with an accuracy of 0.77 and an area under a curve of 0.81, which is higher than the previous studies on the same dataset. GCN model also outperforms several state-of-the-art methods trained at the same time. As far as we know, it is also the first time using the GCN model on this dataset for COPD detection.Graphical abstract

Multimodal emotion recognition in conversation (MERC) involves predicting the emotion category of a conversation on the basis of textual, acoustic, and visual modalities. Information from these diverse modalities can reinforce each other to enhance the accuracy of emotion prediction. However, some information modalities may be absent in real-world applications and information from various modalities may be difficult to integrate. Therefore, a suitable strategy is required to compensate for missing modalities by using information from the available modalities and prioritizing important information. Consequently, this study developed a graph convolutional network (GCN) model with a feature compensation module and dual-channel second-order pooling module for MERC. This model initially uses a GCN to compensate for missing features by aggregating features corresponding to the same utterance node. Subsequently, it applies dual-channel second-order pooling to sift through and integrate all features. Empirical evaluations of the proposed model against other baseline models on two benchmark datasets, namely the IEMOCAP and MELD datasets, indicated that the proposed model outperformed the other models; this finding underscores the effectiveness of the proposed model in recognizing the emotions expressed in multimodal dialogue data.

ABSTRACTLarge‐area, high‐resolution organic light‐emitting diode (OLED) displays require high‐performance oxide thin‐film transistors (TFTs) with excellent mobility and stability, as well as little threshold voltage shift, especially for G8.5+ OLED production lines. Our previous work demonstrated rare‐earth doped oxides are ideal channel layer materials for TFTs due to their superior mobility and stability. However, scaling up TFT production from pilot lines to mass manufacturing poses significant challenges, largely because of the complexity of multi‐element composite materials and intricate processes. In this study, an end‐to‐end graph convolutional network (GCN) model is employed to optimize TFT production processes. By encoding process parameters as graph nodes and features, the GCN effectively predicts critical drain current–gate voltage (I–V) characteristics, enabling precise calculation of electron mobility, subthreshold slope, and threshold voltage. The model achieves a mean absolute percentage error of 19.9% on validation data. Based on the optimized parameters, a TFT device with properties including a mobility of 37.8 cm2/Vs, a threshold voltage of 0.9 V, and a subthreshold slope of 0.62 V/dec was successfully produced. These results highlight the potential of GCN‐based models to address the complexities of TFT mass production, providing a powerful tool for process optimization and performance enhancement.

Entity linking, which maps named entity mentions in a document into the proper entities in a given knowledge graph, has been shown to be able to significantly benefit from modeling the entity relatedness through Graph Convolutional Networks (GCN). Nevertheless, existing GCN entity linking models fail to take into account the fact that the structured graph for a set of entities not only depends on the contextual information of the given document but also adaptively changes on different aggregation layers of the GCN, resulting in insufficiency in terms of capturing the structural information among entities. In this paper, we propose a dynamic GCN architecture to effectively cope with this challenge. The graph structure in our model is dynamically computed and modified during training. Through aggregating knowledge from dynamically linked nodes, our GCN model can collectively identify the entity mappings between the document and the knowledge graph, and efficiently capture the topical coherence among various entity mentions in the entire document. Empirical studies on benchmark entity linking data sets confirm the superior performance of our proposed strategy and the benefits of the dynamic graph structure.

In recent years, substantial progress has been made on Graph Convolutional Networks (GCNs). However, the computing of GCN usually requires a large memory space for keeping the entire graph. In consequence, GCN is not flexible enough, especially for large scale graphs in complex real-world applications. Fortunately, for transductive graph representation learning, methods based on Matrix Factorization (MF) naturally support constructing mini-batches, and thus are more friendly to distributed computing compared with GCN. Accordingly, in this paper, we analyze the connections between GCN and MF, and simplify GCN as matrix factorization with unitization and co-training. Furthermore, under the guidance of our analysis, we propose an alternative model to GCN named Unitized and Co-training Matrix Factorization (UCMF). Extensive experiments have been conducted on several real-world datasets. On the task of semi-supervised node classification, the experimental results illustrate that UCMF achieves similar or superior performances compared with GCN. Meanwhile, distributed UCMF significantly outperforms distributed GCN methods, which shows that UCMF can greatly benefit complex real-world applications.

Graph Neural Networks (GNNs) have become a critical tool for learning on structured data, particularly for their ability to capture feature dependencies within multivariate datasets. This study investigates the application of Graph Convolutional Networks (GCNs) to improve classification performance on such data. We constructed a fully connected graph using cosine similarity to compare our GCN approach against standard dimensionality reduction techniques like PCA and t-SNE. In our tests, the GCN model clearly surpassed these baselines in accuracy, precision, recall, and F1-score.But the method has its own trade-offs: dense graphs are computationally costly, there are chances of overfitting on smaller datasets and the resulting embeddings may be uninterpretable. We cannot make such a claim of generality at this point because this review has only limited itself to one multivariate data set. In future endeavour we would wish to compare GCNs to more powerful classifiers, such as Random Forests, Deep Neural Networks, with greater regard to efficiency in training and resistance to noise. To summarize, this paper confirms that GNNs are an ideal option when it comes to multivariate classification, although scalability and interpretability have to be resolved in order to make it applicable in the real-life context.

LightGCAN: A lightweight graph convolutional attention network for user preference modeling and personalized recommendation

The cross-modal retrieval task has raised continuous attention in recent years with the increasing scale of multi-modal data, which has broad application prospects including multimedia data management and intelligent search engine. Most existing methods mainly project data of different modalities into a common representation space where label information is often exploited to distinguish samples from different semantic categories. However, they typically treat each label as an independent individual and ignore the underlying semantic structure of labels. In this paper, we propose an end-to-end adaptive label-aware graph convolutional network (ALGCN) by designing both the instance representation learning branch and the label representation learning branch, which can obtain modality-invariant and discriminative representations for cross-modal retrieval. Firstly, we construct an instance representation learning branch to transform instances of different modalities into a common representation space. Secondly, we adopt Graph Convolutional Network (GCN) to learn inter-dependent classifiers in the label representation learning branch. In addition, a novel adaptive correlation matrix is proposed to efficiently explore and preserve the semantic structure of labels in a data-driven manner. Together with a robust self-supervision loss for GCN, the GCN model can be supervised to learn an effective and robust correlation matrix for feature propagation. Comprehensive experimental results on three benchmark datasets, NUS-WIDE, MIRFlickr and MS-COCO, demonstrate the superiority of ALGCN, compared with the state-of-the-art methods in cross-modal retrieval.

CLP-GCN: Confidence and label propagation applied to Graph Convolutional Networks

Semi-supervised learning with a graph-based approach has gained prominence in machine learning, particularly in scenarios where labeling data involves substantial costs. Graph convolution networks (GCNs) have found widespread application in semi-supervised learning, predominantly on graph-structured data such as citation and social networks. However, a noticeable gap exists in the application of these methods to non-graph multi-view data, such as collections of images. In an effort to address this gap, we introduce two innovative deep semi-supervised multi-view classification models specifically tailored for non-graph data. Both models share a common architecture, leveraging GCNs and integrating a label smoothing constraint. The primary distinction lies in the construction of the consensus similarity graph. The first model directly reconstructs the consensus graph from different views using a specialized objective function designed for flexible graph-based semi-supervised classification. In contrast, the second model independently reconstructs individual graphs and subsequently adaptively merges them into a unified consensus graph. Our experiments encompass various multiple-view image datasets. The results consistently demonstrate the superior performance of our proposed approach compared to traditional fusion methods with GCNs. In this research, we present two approaches for tackling semi-supervised classification challenges involving multiple views. One method is named Semi-supervised Classification with a Unified Graph (SCUG), and the other is referred to as Semi-supervised Classification with a Fused Graph (SC-Fused). Both methods share a common semi-supervised classification process, utilizing the GCN framework and incorporating label smoothing. However, the key distinction lies in the construction of the similarity graph. Unlike traditional ad hoc graph construction approaches, our proposed methods, SCUG and SC-Fused, estimate the unified graph or individual graphs, respectively, alongside the labels. This results in more optimized graphs that benefit from data smoothing and the semi-supervised context.