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Automation and Computation (AUTOCOM), International Conference on

作者： Marri Sireesha Laith Hussein M. Manju Prashant Johri T. Kuppuraj Aanandha Saravanan K Department of Computer Science and Engineering CMR Technical Campus Hyderabad India Department of Computers Techniques Engineering College of technical engineering The Islamic University Najaf Iraq Department of Computer Science Kongu Arts and Science College Erode Tamilnadu India School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore India Department of ECE Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Due to the increases pest impacts on agricultural productivity, effective pest prediction models are required for efficient pest control in precision farming. The framework outlined in this research uses a graph convolutional Network (GCN) to estimate pest infestation by exploiting spatial and temporal relationships present in agri-ecosystems. The methodology combines weather, soil, pest, and crop health data into a graph-based representation of spatial units, and the directed connections between them that reflect their proximity and climatic similarity. The GCN converts these graphs to learn such complex relationship as the infestation prediction at various severity levels. The proposed model has produced a better accuracy of 94.8%, better precision of 95.2%, better recall of 94.5% and better F1-score of 94.8% as compared from Random forest, SVM, LSTM, CNN as per the comparison of table 3. Comparing them focuses on graph construction approaches and temporal aspects, mixed P/S and temporal emb has improved the model performance. This framework is practical for further pest management and employing the result without harming the environment. The results show the effectiveness of GCNs for use in pest prediction in precision farming and the opportunity to develop sustainable and scalable data-based agriculture.

关键词： Support vector machines Accuracy graph convolutional networks Biological system modeling Stability criteria Crops Soil Agriculture Farming Meteorology

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graph convolutional networks for Semi-Supervised Image Segmentation

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IEEE ACCESS 2022年 10卷 104144-104155页

作者： Fabijanska, Anna Lodz Univ Technol Inst Appl Comp Sci PL-90537 Lodz Poland

The problem of image segmentation is one of the most significant ones in computer vision. Recently, deep-learning methods have dominated state-of-the-art solutions that automatically or interactively divide an image into subregions. However, the limitation of deep-learning approaches is that they require a substantial amount of training data, which is costly to prepare. An alternative solution is semi-supervised image segmentation. It requires rough denotations to define constraints that are next generalized to precisely delimit relevant image regions without using train examples. Among semi-supervised strategies for image segmentation, the leading are graph-based techniques that define image segmentation as a result of pixel or region affinity graph partitioning. This paper revisits the problem of graph-based image segmentation. It approaches the problem as semi-supervised node classification in the SLIC superpixels region adjacency graph using a graph convolutional network (GCN). The performance of both spectral and spatial graph convolution operators is considered, represented by Chebyshev convolution operator and graphSAGE respectively. The results of the proposed method applied to binary and multi-label segmentation are presented, numerically assessed, and analyzed. In its best variant, the proposed method scored the average DICE of 0.86 in the binary segmentation task and 0.79 in the multi-label segmentation task. Comparison with state-of-the-art graph-based methods, including Random Walker and GrabCut, shows that graph convolutional networks can represent an attractive alternative to the existing solutions to graph-based semi-supervised image segmentation.

关键词： Image segmentation Clustering methods convolutional neural networks Image edge detection graph neural networks Laplace equations Semi-supervised learning GCN graph convolutional networks graph node clustering region adjacency graph semi-supervised image segmentation

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graph convolutional networks of reconstructed graph structure with constrained Laplacian rank

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第24期81卷 34183-34194页

作者： Zhan, Mengmeng Gan, Jiangzhang Lu, Guangquan Wan, Yingying Guangxi Normal Univ Coll Comp Sci & Informat Technol Guilin 541004 Guangxi Peoples R China Massey Univ Sch Nat & Computat Sci Auckland Campus Auckland 0745 New Zealand

convolutional neural networks (CNNs) have achieved unprecedented competitiveness in text and two-dimensional image data processing because of its good accuracy performance and high detection speed. graph convolutional networks (GCNs), as an extension of classical CNNs in graph data processing, have attracted wide attention. At present, GCNs often use domain knowledge (such as citation recommendation system, biological cell networks) or artificial created fixed graph to achieve various semi-supervised classication tasks. Poor quality graph will lead to suboptimal results of semi-supervised classification tasks. We propose a more general GCN of reconstructed graph structure with constrained Laplacian rank. First, we use hypergraph to establish multivariate relationships between data. On the basis of the hypergraph, In virtue of Laplacian rank constraint to the graph matrix, we learn a new graph structure which hascconnected components (wherecis the number of classification), and then we construct an ideal graph matrix which is more suitable for the task of semi-supervised classification on GCNs. Finally, the data and the new graph are input GCNs model to get the results of classification. Experiments on 10 different datasets demonstrate that this method is more competitive than the comparison method.

关键词： graph convolutional networks Adaptive graph Hypergraph Semi-supervised classification graph structure

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graph convolutional networks for Model-Based Learning in Nonlinear Inverse Problems

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2021年 7卷 1341-1353页

作者： Herzberg, William Rowe, Daniel B. Hauptmann, Andreas Hamilton, Sarah J. Marquette Univ Dept Math & Stat Sci Milwaukee WI 53233 USA Univ Oulu Res Unit Math Sci Oulu 90570 Finland UCL Dept Comp Sci London WC1E 6BT England

The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solved on nonuniform meshes, arising from a finite element method typical for nonlinear inverse problems, interpolation and embeddings are needed. To overcome this, we present a flexible framework to extend model-based learning directly to nonuniform meshes, by interpreting the mesh as a graph and formulating our network architectures using graph convolutional neural networks. This gives rise to the proposed iterative graph convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh. We present results for Electrical Impedance Tomography, a severely ill-posed nonlinear inverse problem that is frequently solved via optimization-based methods, where the forward problem is solved by finite element methods. Results for absolute EIT imaging are compared to standard iterative methods as well as a graph residual network. We show that the GCNM has good generalizability to different domain shapes andmeshes, out of distribution data as well as experimental data, from purely simulated training data and without transfer training.

关键词： Finite element method graph convolutional networks model-based deep learning conductivity electrical impedance tomography

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graph convolutional networks: analysis, improvements and results

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APPLIED INTELLIGENCE 2022年第8期52卷 9033-9044页

作者： Ullah, Ihsan Manzo, Mario Shah, Mitul Madden, Michael G. Univ Coll Dublin UCD CeADAR Irelands Ctr Appl AI Dublin Ireland Univ Naples LOrientale Informat Technol Serv I-80121 Naples Italy Natl Univ Ireland Sch Comp Sci Machine Learning & Data Min Grp Galway Ireland

A graph can represent a complex organization of data in which dependencies exist between multiple entities or activities. Such complex structures create challenges for machine learning algorithms, particularly when combined with the high dimensionality of data in current applications. graph convolutional networks were introduced to adopt concepts from deep convolutional networks (i.e. the convolutional operations/layers) that have shown good results. In this context, we propose two major enhancements to two of the existing graph convolutional network frameworks: (1) topological information enrichment through clustering coefficients;and (2) structural redesign of the network through the addition of dense layers. Furthermore, we propose minor enhancements using convex combinations of activation functions and hyper-parameter optimization. We present extensive results on four state-of-art benchmark datasets. We show that our approach achieves competitive results for three of the datasets and state-of-the-art results for the fourth dataset while having lower computational costs compared to competing methods.

关键词： graph convolutional networks Clustering coefficients Dimensionality reduction

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graph convolutional networks with the self-attention mechanism for adaptive influence maximization in social networks

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COMPLEX & INTELLIGENT SYSTEMS 2024年第6期10卷 8383-8401页

作者： Tang, Jianxin Song, Shihui Du, Qian Yao, Yabing Qu, Jitao Lanzhou Univ Technol Sch Comp & Commun 287 Langongping Rd Lanzhou 730050 Peoples R China

The influence maximization problem that has drawn a great deal of attention from researchers aims to identify a subset of influential spreaders that can maximize the expected influence spread in social networks. Existing works on the problem primarily concentrate on developing non-adaptive policies, where all seeds will be ignited at the very beginning of the diffusion after the identification. However, in non-adaptive policies, budget redundancy could occur as a result of some seeds being naturally infected by other active seeds during the diffusion process. In this paper, the adaptive seeding policies are investigated for the intractable adaptive influence maximization problem. Based on deep learning model, a novel approach named graph convolutional networks with self-attention mechanism (ATGCN) is proposed to address the adaptive influence maximization as a regression task. A controlling parameter is introduced for the adaptive seeding model to make a tradeoff between the spreading delay and influence coverage. The proposed approach leverages the self-attention mechanism to dynamically assign importance weight to node representations efficiently to capture the node influence feature information relevant to the adaptive influence maximization problem. Finally, intensive experimental findings on six real-world social networks demonstrate the superiorities of the adaptive seeding policy over the state-of-the-art baseline methods to the conventional influence maximization problem. Meanwhile, the proposed adaptive seeding policy ATGCN improves the influence spread rate by up to 7% in comparison to the existing state-of-the-art greedy-based adaptive seeding policy.

关键词： Adaptive seeding policy Deep learning graph convolutional networks Influence maximization Self-attention mechanism Social networks

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graph convolutional networks for traffic forecasting with missing values

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DATA MINING AND KNOWLEDGE DISCOVERY 2023年第2期37卷 913-947页

作者： Zuo, Jingwei Zeitouni, Karine Taher, Yehia Garcia-Rodriguez, Sandra Technol Innovat Inst Abu Dhabi 9639 U Arab Emirates Univ Paris Saclay UVSQ DAVID Lab Versailles France CEA Data Anal & Syst Intelligence Lab LIST Gif Sur Yvette France

Traffic forecasting has attracted widespread attention recently. In reality, traffic data usually contains missing values due to sensor or communication errors. The Spatio-temporal feature in traffic data brings more challenges for processing such missing values, for which the classic techniques (e.g., data imputations) are limited: (1) in temporal axis, the values can be randomly or consecutively missing;(2) in spatial axis, the missing values can happen on one single sensor or on multiple sensors simultaneously. Recent models powered by graph Neural networks achieved satisfying performance on traffic forecasting tasks. However, few of them are applicable to such a complex missing-value context. To this end, we propose GCN-M, a graph convolutional Network model with the ability to handle the complex missing values in the Spatio-temporal context. Particularly, we jointly model the missing value processing and traffic forecasting tasks, considering both local Spatio-temporal features and global historical patterns in an attention-based memory network. We propose as well a dynamic graph learning module based on the learned local-global features. The experimental results on real-life datasets show the reliability of our proposed method.

关键词： Traffic forecasting Missing values graph convolutional networks Memory networks Neural networks Deep learning

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graph convolutional networks: a comprehensive review

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Computational Social networks 2019年第1期6卷 1-23页

作者： Zhang, Si Tong, Hanghang Xu, Jiejun Maciejewski, Ross University of Illinois Urbana-Champaign Champaign United States HRL Laboratories LLC Malibu United States Arizona State University Tempe United States

graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research. © 2019, The Author(s).

关键词： Aggregation mechanism Deep learning graph convolutional networks graph representation learning Spatial methods Spectral methods

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graph convolutional networks for Student Answers Assessment 23rd

Graph Convolutional Networks for Student Answers Assessment

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23rd Annual International Conference on Text, Speech, and Dialogue (TSD)

作者： Khayi, Nisrine Ait Rus, Vasile Univ Memphis Inst Intelligent Syst Memphis TN 38152 USA

ISBN: (纸本)9783030583231;9783030583224

graph convolutional networks have achieved impressive results in multiple NLP tasks such as text classification. However, this approach has not been explored yet for the student answer assessment task. In this work, we propose to use graph convolutional networks to automatically assess freely generated student answers within the context of dialogue-based intelligent tutoring systems. We convert this task to a node classification task. First, we build a DTGrade graph where each node represents the concatenation of the student answer and its corresponding reference answer whereas the edges represent the relatedness between nodes. Second, the DTGrade graph is fed to two layers of graph convolutional networks. Finally, the output of the second layer is fed to a softmax layer. The empirical results showed that our model reached the state-of-the-art results by obtaining an accuracy of 73%.

关键词： graph convolutional networks Student answers assessment Intelligent tutoring systems

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graph convolutional networks Based on Neighborhood Expansion 7

Graph Convolutional Networks Based on Neighborhood Expansion

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7th International Conference on Electronic Information Technology and Computer Engineering (EITCE)

作者： Gong, Shengchao Weng, Wei Hou, Fengxia Lin, Dongsheng Wen, Juan Xiamen Univ Technol Xiamen Fujian Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen Peoples R China Xiamen Univ Xiamen Fujian Peoples R China

ISBN: (纸本)9798400708305

graph neural networks (GNNs) are an efficient framework for learning graph-structured data and achieving state-of-the-art performance on many tasks, including node classification, link prediction, and graph classi-fication. Over-smoothing problem is troublesome in GNNs, that is, increasing the depth of the network leads to significant degradation in model performance. A large number of methods address it through regu-larization or residual concatenation, but these methods only mitigate the over-smoothing in some extent, which persists as the network depth increases. In this paper, we propose the graph convolutional Network Based on Neighborhood Expansion (NEGCN), which enables the shallow network aggregate multi-hop neighbor features information for node embedding. specifically, NEGCN uses Depth-First-Search to ex-pand 1-hop neighbors with similarity, so as to avoid the problem of over-smoothing. The robustness of NEGCN is further improved by combining with attention mechanism and graph sparsification methods. Extensive experiments demonstrate that our model has a superior performance on real-world graph learning benchmarks.

关键词： graph convolutional networks graph Attention networks Neighborhood expansion Over-smoothing

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