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Combining graph convolutional neural networks and Label Propagation

ACM TRANSACTIONS ON INFORMATION SYSTEMS 2022年第4期40卷 1–27页

作者： Wang, Hongwei Leskovec, Jure Stanford Univ Comp Sci Dept Stanford CA 94305 USA

Label Propagation Algorithm (LPA) and graph convolutional neural networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification, but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information. However, while conceptually similar, theoretical relationship between LPA and GCN has not yet been systematically investigated. Moreover, it is unclear how LPA and GCN can be combined under a unified framework to improve the performance. Here we study the relationship between LPA and GCN in terms of feature/label Influence, in which we characterize how much the initial feature/label of one node influences the final feature/label of another node in GCN/LPA. Based on our theoretical analysis, we propose an end-to-end model that combines GCN and LPA. In our unified model, edge weights are learnable, and the LPA serves as regularization to assist the GCN in learning proper edge weights that lead to improved performance. Our model can also be seen as learning the weights of edges based on node labels, which is more direct and efficient than existing feature-based attention models or topology-based diffusion models. In a number of experiments for semi-supervised node classification and knowledge-graph-aware recommendation, our model shows superiority over state-of-the-art baselines.

关键词： graph convolutional neural networks label propagation algorithm semi-supervised learning

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Phase-Locking Value Based graph convolutional neural networks for Emotion Recognition

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IEEE ACCESS 2019年 7卷 93711-93722页

作者： Wang, Zhongmin Tong, Yue Heng, Xia Xian Univ Posts & Telecommun Sch Comp Sci & T Imol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent P Xian 710121 Shaanxi Peoples R China

Recognition of discriminative neural signatures and regions corresponding to emotions are important in understanding the neuron functional network underlying the human emotion process. Electroencephalogram (EEG) is a spatial discrete signal. In this paper, in order to extract the spatio-temporal characteristics and the inherent information implied by functional connections, a multichannel EEG emotion recognition method based on phase-locking value (PLV) graph convolutional neural networks (P-GCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is using PLV-based brain network to model multi-channel EEG features as graph signals and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed P-GCNN method uses the PLV connectivity of EEG signals to determine the mode of emotional-related functional connectivity, which is used to represent the intrinsic relationship between EEG channels in different emotional states. On this basis, the neural network is trained to extract effective EEG emotional features. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DEAP dataset. The experimental results demonstrate that novel framework can improve the classification accuracy on both datasets, but not so effective on DEAP as on SEED, in which with 84.35% classification accuracy for SEED, and the average accuracies of 73.31%, 77.03% and 79.20% are, respectively, obtained for valence, arousal, and dominance classifications on the DEAP database.

关键词： EEG emotion recognition phase-locking value graph convolutional neural networks brain network functional connectivity

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Prediction and Interpretability of Glass Transition Temperature of Homopolymers by Data-Augmented graph convolutional neural networks

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ACS APPLIED MATERIALS & INTERFACES 2023年第46期15卷 54006-54017页

作者： Hu, Junyang Li, Zean Lin, Jiaping Zhang, Liangshun East China Univ Sci & Technol Sch Mat Sci & Engn Shanghai Key Lab Adv Polymer Mat Shanghai 200237 Peoples R China

Establishing the structure-property relationship by machine learning (ML) models is extremely valuable for accelerating the molecular design of polymers. However, existing ML models for the polymers are subject to scarcity issues of training data and fewer variations of graph structures of molecules. In addition, limited works have explored the interpretability of ML models to infer the latent knowledge in the field of polymer science that could inspire ML-assisted molecular design. In this contribution, we integrate graph convolutional neural networks (GCNs) with data augmentation strategy to predict the glass transition temperature T-g of polymers. It is demonstrated that the data-augmented GCN model outperforms the conventional models and achieves a higher accuracy for the prediction of T-g despite a small amount of training data. Furthermore, taking advantage of molecular graph representations, the data-augmented GCN model has the capability to infer the importance of atoms or substructures from the understanding of T-g, which generally agrees with the experimental findings in the field of polymer science. The inferred knowledge of the GCN model is used to advise on the design of functional polymers with specific T-g. The data-augmented GCN model possesses prominent superiorities in the establishment of structure-property relationship and also provides an efficient way for accelerating the rational design of polymer molecules.

关键词： machine learning graph convolutional neural networks glass transition temperature structure-propertyrelationship interpretability

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Deep Residual Split Directed graph convolutional neural networks for Action Recognition

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IEEE MULTIMEDIA 2020年第4期27卷 9-17页

作者： Fu, Bo Fu, Shilin Wang, Liyan Dong, Yuhan Ren, Yonggong Liaoning Normal Univ Dalian Peoples R China

Human action recognition is the basis technology of human behavior understanding, and it is a research hotspot in the field of computer vision. Recently, some studies show skeleton data (i.e., joint points and edges) is naturally more conducive to mining the connotation of human action, so exploring the relationship between joints and bones is helpful to improve action recognition. In this article, regarding skeleton data as a directed graph, we design directed graph convolutional neural networks with a novel residual split structure. First, we construct a directed graph represent model to extract human behavior by two kinds of graph models. Second, we use a novel residual split block to construct graph convolution neural network. Different from the traditional residual networks, we split high-dimensional features into several shallow features with the same dimension. It can not only ensure the diversity of mining features, but also avoid the gradient disappearing. Finally, during training, we use random sampling of data to reduce the burden of network training. Experiment results show that the proposed method achieves higher recognition rate than the comparative methods on the NTU-RGBD dataset.

关键词： neural networks convolutional neural networks Training Data mining Feature extraction graph convolutional neural networks Human action recognition Residual-split block

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graph convolutional neural networks-based assessment of students' collaboration ability

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第28期34卷

作者： Lin, Jinjiao Gao, Tianqi Wen, Yuhua Yu, Xianmiao You, Bizhen Yin, Yanfang Zhao, Yanze Pu, Haitao Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan Peoples R China Shandong Police Coll Legal Teaching & Res Dept Jinan Peoples R China Shandong Univ Sci & Technol Dept Elect Informat Jinan Peoples R China

As 21st-century skills have become increasingly important, collaboration ability is now considered essential in many areas of life. Different theoretical frameworks and assessment tools have emerged to measure this skill. However, more applied studies on its implementation and assessment in current educational settings are required. This research accordingly uses graph convolutional neural networks (GCNs) to assess students' collaboration ability from students' assignments. The Pearson correlation coefficient is used to measure the similarity of the level of students' collaboration ability, and similar students are linked together to establish an adjacency matrix. By sorting through relevant literature and selecting the feature words that represent the strength of collaboration ability, calculating the similarity between the preprocessed student data and each selected feature word, after which the highest value of the similarity as the feature value of the student for this feature and establish the student feature matrix. Finally, the GCNs are jointly trained by the adjacency matrix and the feature matrix. The results show that this method can effectively assess students' collaboration ability. Moreover, compared with other text classification methods, the GCNs selected in this paper has higher accuracy.

关键词： collaboration ability graph convolutional neural networks self-analysis text classification

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An Energy Harvesting Roadside Unit communication load prediction and energy scheduling based on graph convolutional neural networks for spatial-temporal vehicle data

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IET SIGNAL PROCESSING 2022年第8期16卷 909-924页

作者： Ding, Xu Zheng, Hang Wang, Yang Zhao, Chong Yang, Fan Hefei Univ Technol Sch Comp & Informat Inst Ind & Equipment Technol Hefei 230009 Anhui Peoples R China Hefei Univ Technol Sch Comp & Informat Technol Hefei Anhui Peoples R China

The Energy Harvesting Roadside Unit (EH-RSU) with self-powered module will not only effectively reduce the communication load of regional Vehicular Ad Hoc networks, but also enjoys a low deployment cost. Given the imbalance in communication demands invoked by transportation systems, the EH-RSU should allocate energy appropriately in accordance with its energy harvesting rate to ensure the communication safety of vehicles within its coverage. Firstly, we propose a novel attention-based spatial-temporal graph convolutional network (ASTGCN) to predict the communication load around the EH-RSU in the road network through the surrounding vehicle information. Secondly, we use the predicted communication load as part of the input parameters to neural network and leverage a double deep Q network to ameliorate the operating states switching strategy of EH-RSUs by reinforcement learning so that they achieve a more satisfying effective time with limited resources. Finally, we built a dataset by simulation to validate the effectiveness of our model. The results show that our prediction model has a better accuracy and the improved strategy has higher efficiency compared with other methods.

关键词： surrounding vehicle information double deep Q network EH-RSU regional Vehicular Ad Hoc networks attention-based spatial-temporal graph convolutional network graph theory intelligent transportation systems deep learning (artificial intelligence) energy harvesting graph convolutional neural networks predicted communication load energy harvesting rate convolutional neural nets communication demands road traffic neural network low deployment cost reinforcement learning spatial-temporal vehicle data vehicular ad hoc networks Energy Harvesting Roadside Unit communication load prediction prediction model road network traffic engineering computing

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Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks

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JOURNAL OF TRANSLATIONAL MEDICINE 2024年第1期22卷 1-12页

作者： Nguyen, Trong Hieu Doan, Nhu Nhat Tan Tran, Trung Hieu Huynh, Le Anh Khoa Doan, Phuoc Loc Nguyen, Thi Hue Hanh Nguyen, Van Thien Chi Nguyen, Giang Thi Huong Nguyen, Hoai-Nghia Giang, Hoa Tran, Le Son Phan, Minh Duy Gene Solut Med Genet Inst Ho Chi Minh Vietnam Virginia Commonwealth Univ Sch Med Dept Biostat Richmond VA USA

BackgroundCell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA *** constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of *** final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA *** conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

关键词： Tissue of origin cfDNA Tumor-specific methylation atlas Genome-wide methylation density graph convolutional neural networks

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Management and Monitoring of Multi-Behavior Recommendation Systems Using graph convolutional neural networks

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INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE 2022年第6N07期33卷 583-601页

作者： Liu, Changwei Wang, Kexin Wu, Aman Guangzhou Nanyang Polytech Coll Dept Informat Engn Guangzhou Peoples R China Heilongjiang Univ Sch Comp Sci & Technol Harbin Peoples R China

Different recommendation algorithms, which often use only a single type of user-item engagement, are plagued by imbalanced datasets and cold start problems. Multi-behavior recommendations, which takes advantage of a variety of customer interaction including click and favorites, can be a good option. Early attempts at multi-behavior suggestion tried to consider the varying levels of effect each behavior has on the target behavior. They also disregard the meanings of behaviors, which are implicit in multi-behavior information. Because of these two flaws, the information isn't being completely utilized to improve suggestion performance on the specific behavior. In this paper, we take a novel response to the situation by creating a unified network to capture multi-behavior information and displaying the MBGCNNN model (Multi-Behavior graph convolutional neural Network). MBGCNN may effectively overcome the constraints of prior studies by learning behavior intensity via the user-item dissemination level and collecting behavior interpretation via the items dissemination level. Practical derives from various data sets back up our model's order to leverage multi-behavior data. On real methods, our approach beats the average background by 25.02 percent and 6.51 percent, respectively. Additional research on cold-start consumers supports the viability of our suggested approach.

关键词： Multi-behavior-recommendations collaboration filter graph convolutional neural networks

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Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2022年第1期19卷 1-14页

作者： Filtjens, Benjamin Ginis, Pieter Nieuwboer, Alice Slaets, Peter Vanrumste, Bart Katholieke Univ Leuven Dept Elect Engn ESAT eMedia Res Lab STADIUS Andreas Vesaliusstr 13 B-3000 Leuven Belgium Katholieke Univ Leuven Dept Mech Engn Intelligent Mobile Platforms Res Grp Andreas Vesaliusstr 13 B-3000 Leuven Belgium Katholieke Univ Leuven Dept Rehabil Sci Res Grp Neurorehabil eNRGy Tervuursevest 101 B-3001 Heverlee Belgium

Background Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes an automated motion-capture-based FOG assessment method driven by a novel deep neural network. Methods Automated FOG assessment can be formulated as an action segmentation problem, where temporal models are tasked to recognize and temporally localize the FOG segments in untrimmed motion capture trials. This paper takes a closer look at the performance of state-of-the-art action segmentation models when tasked to automatically assess FOG. Furthermore, a novel deep neural network architecture is proposed that aims to better capture the spatial and temporal dependencies than the state-of-the-art baselines. The proposed network, termed multi-stage spatial-temporal graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical spatial-temporal motion among the joints inherent to motion capture, while the multi-stage component reduces over-segmentation errors by refining the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects. Results The experiments indicate that the proposed model outperforms four state-of-the-art baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an excellent (r = 0.93 [0.87, 0.97]) and moderately strong (r = 0.75 [0.55, 0.87]) linear relationship with FOG outcomes derived from manual annotations. Conclusions The proposed MS-GCN may provide an automated and objective alternative to labor-intensive clinician-based FOG assessment. Future work is now possible that aims to assess the generalization of MS-GCN to a larger and more varied verification co

关键词： Temporal convolutional neural networks graph convolutional neural networks Freezing of gait Parkinson's disease MS-GCN

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CHARACTERIZING RESIDENTIAL BUILDING PATTERNS IN HIGH-DENSITY CITIES USING graph convolutional neural networks 29th

CHARACTERIZING RESIDENTIAL BUILDING PATTERNS IN HIGH-DENSITY...

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29th International Conference of the Association-for-Computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Jia, Muxin Zhang, Kaiheng Narahara, Taro New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9789887891826

In urban morphology studies, accurately classifying residential building patterns is crucial for informed zoning and urban design guidelines. While machine learning, particularly neural networks, has been widely applied to urban form taxonomy, most studies focus on grid-like data from street-view images or satellite imagery. Our paper provides a novel framework for graph classification by extracting features of clustering buildings at different scales and training a spectral-based GCN model on graph-structured data. Furthermore, from the perspective of urban designers, we put forward corresponding design strategies for different building patterns through data visualization and scenario analysis. The findings indicate that GCN has a good performance and generalization ability in identifying residential building patterns, and this framework can aid urban designers or planners in decision-making for diverse urban environments in Asia.

关键词： Urban morphology Machine learning Building pattern classification graph convolutional neural networks

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