Showing 1-25 of 1,071 resultsfor "Index Terms":graph convolutional networks (GCNs)
"Index Terms":graph convolutional networks (GCNs)
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Graph-Based Representation and Detection of Cyberbullying on Social Media Using GCNs
Sachin K Rao;Akshay Paramesha;D Adithya;K S Vineet;Aaryan;Rachana H Dharani
2024 Asian Conference on Intelligent Technologies (ACOIT)
Year: 2024 | Conference Paper |
The widespread use of social media has contributed to an increase in instances of cyberbullying, which constitutes hateful and offensive interactions between users of digital platforms, necessitating effective detection methods to mitigate its impact. This study presents a novel approach to cyberbullying detection by employing Graph Convolutional Networks (GCNs), which are adept at processin...Show More
Block Krylov Subspace Methods for Scalable Deep Graph Convolutional Networks
2025 International Conference on Emerging Systems and Intelligent Computing (ESIC)
Year: 2025 | Conference Paper |
In this paper, I explore the application of block Krylov subspace methods to enhance the scalability and efficiency of deep graph convolutional networks (GCNs). Deep learning models, particularly GCNs, have gained significant attention for their capability to process graph-structured data; however, they often face challenges related to scalability, over smoothing, and the preservation ...Show More
Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks
Razib Hayat Khan;Jonayet Miah;S M Yasir Arafat;M M Mahbubul Syeed;Duc M Ca
2023 15th International Conference on Innovations in Information Technology (IIT)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
This study delves into the application of Graph Neural Networks (GNNs) in the realm of traffic forecasting, a crucial facet of intelligent transportation systems. Accurate traffic predictions are vital for functions like trip planning, traffic control, and vehicle routing in such systems. Three prominent GNN architectures—Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggre...Show More
ScaleGCN: Efficient and Effective Graph Convolution via Channel-Wise Scale Transformation
Graph convolutional networks (GCNs) have shown success in many graph-based applications as they can combine node features and graph topology to obtain expressive embeddings. While there exist numerous GCN variants, a typical graph convolution layer uses neighborhood aggregation and fully-connected (FC) layers to extract topological and node-wise features, respectively. However, when the rece...Show More
Graph convolution can process non-Euclidean structural data and has some applications in hyperspectral image classification problems in recent years. However, existing Graph Convolutional Network (GCN) methods are generally conducted on the fixed neighborhood receptive. Thus, the multi-scale spatial information is not fully taken into account, which limits their classification performances on HSI ...Show More
A Quantum Spatial Graph Convolutional Neural Network Model on Quantum Circuits
This article proposes a quantum spatial graph convolutional neural network (QSGCN) model that is implementable on quantum circuits, providing a novel avenue to processing non-Euclidean type data based on the state-of-the-art parameterized quantum circuit (PQC) computing platforms. Four basic blocks are constructed to formulate the whole QSGCN model, including the quantum encoding, the quantum grap...Show More
GATreg - Graph Attention Networks with Regularization
2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
Year: 2025 | Conference Paper |
To improve the applicability and performance of graph neural networks (GNNs); graph convolution networks (GCNs) and graph attention networks (GATs) have shown promising ways forward. However, lack of generalizability has been a major bottleneck for their widespread applications. To overcome this limitation of GNNs, we propose a regularization scheme for GAT, termed as GATreg. We use a novel ...Show More
GDCNet: Graph Enrichment Learning via Graph Dropping Convolutional Networks
Graph convolutional networks (GCNs) have been widely studied to address graph data representation and learning. In contrast to traditional convolutional neural networks (CNNs) that employ many various (spatial) convolution filters to obtain rich feature descriptors to encode complex patterns of image data, GCNs, however, are defined on the input observed graph $G(\mathbf {X},\mathbf {...Show More
Hardware Acceleration for GCNs via Bidirectional Fusion
Han Li;Mingyu Yan;Xiaocheng Yang;Lei Deng;Wenming Li;Xiaochun Ye;Dongrui Fan;Yuan Xie
IEEE Computer Architecture Letters
Cited by: Papers (4)
Derived from the fusion of graph traversal and neural networks, graph convolutional neural networks (GCNs) have achieved state-of-the-art performance in graph learning. However, the hybrid execution pattern, caused by the opposite characteristics of graph traversal based phase and neural network based transformation phase, poses huge challenges to the efficient execution of traditional archi...Show More
HDECGCN: A Heterogeneous Dual Enhanced Network Based on Hybrid CNNs Joint Multiscale Dynamic GCNs for Hyperspectral Image Classification
A hyperspectral image (HSI) classification algorithm that combines graph convolutional networks (GCNs) and convolutional neural networks (CNNs) aims to generate complementary spatial-spectral joint information at the superpixel and pixel levels. However, the CNN part is typically a single 2-D or 3-D network that cannot fully capture the middle or long-range spatial relationships between pixe...Show More
Integration of GAT and GCN for WiFi Positioning
2024 International Symposium on Antennas and Propagation (ISAP)
Year: 2024 | Conference Paper |
In this paper, we propose a novel algorithm that combines graph attention network (GAT) with graph convolutional network (GCN), both of which are types of graph neural networks (GNNs). GNNs have gained attention for their ability to overcome the non-line-of-sight (NLoS) bias in WiFi positioning by efficiently extracting local features from graphs. Existing GNN-based algorithms often rely on GCN...Show More
DualGCN: A Convolutional Network for Anomaly Detection in Directed Graphs Based on Symmetrized and Skew-Symmetrized Laplacians
2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP)
Year: 2024 | Conference Paper |
Graph machine learning techniques and notably graph neural networks (GNNs) have seen a surge in popularity due to the suitability of graphs for being the underlying data structure in a multitude of applications. Spectral graph convolutional networks (GCNs), however, seem to encounter shortfalls when it comes to directed graphs. The root cause lies in the asymmetric nature of the adjacency ma...Show More
Research on Recommendation Algorithm of Graph Neural Network Based on Multi-Mode Fusion
2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)
Year: 2024 | Conference Paper |
This paper introduces a graph neural network recommendation algorithm based on multi-modal fusion, aimed at enhancing recommendation system performance by integrating image features with graph structure features. The central approach of this research involves using Convolutional Neural Networks (CNNs) to extract image features, Graph Convolutional Networks (GCNs) to capture graph structure f...Show More
To ensure the operation safety of complex industrial processes, faults that occur in the process should be detected and identified in time to avoid further catastrophic events. Therefore, fault diagnosis plays an indispensable role in the process industry. With the expansion of the production scale of industrial processes, the structural attributed graph is suitable for describing the process data...Show More
Research on Graph Convolutional Neural Networks with Integrated Attention Mechanisms in Node Classification Tasks
2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)
Year: 2024 | Conference Paper |
Graph Convolutional Networks (GCNs) have demonstrated powerful performance in the task of node classification of graph-structured data. However, traditional GCN methods typically only consider information aggregation of immediate neighbors and assign the same weight to all neighbors, which limits the model's ability to capture complex relationships and long-distance dependencies. To solve th...Show More
Sarcasm Detection in X Data Using Node Embedding and Graph Convolutional Networks
Aditya Dayal Tyagi;Setu Garg;Swati Sharma;Vivek Tomar;Kimmi Verma
2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
An extensive amount of attention has been paid to the implications that the detection of sarcasm on online social networks such as Facebook, X etc. could have for the reason of sarcasm detection, sentiment analysis, content regulation, and public opinion. In light of the fact that traditional methods frequently struggle to comprehend the nuances of sarcasm, there is a growing interest in more adva...Show More
Enhanced Graph Convolutional Networks with Adaptive Aggregation
2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT)
Year: 2025 | Conference Paper |
Graph neural networks represent the latest promising advances in deep learning models to analyze graph-structured data, but traditional models, such as the Graph Convolutional Network, have scalability issues such as oversmoothing and lack of generalization. This work extends the standard GCN by proposing adaptive aggregation functions, scalable normalization techniques, and robust regularization ...Show More
Aspect-Aware Graph Attention Network for Heterogeneous Information Networks
Graph Convolutional Networks (GCNs) derive inspiration from recent advances in computer vision, by stacking layers of first-order filters followed by a nonlinear activation function to learn entity or graph embeddings. Although GCNs have been shown to boost the performance of many network analysis tasks, they still face tremendous challenges in learning from Heterogeneous Information N...Show More
DrugSenti-Rx: Analyzing the Sentiment Landscape in Drug Reviews via Aspect Infused Graph Convolutional Networks
Anirban Saha;Yash Agarwal;Avigyan Chowdhury;Shamina Kaushar;Nabanita Das;Bikash Sadhukhan
2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET)
Year: 2024 | Conference Paper |
Sentiment analysis (SA) has become more important in drug evaluations because to its significance to healthcare professionals, pharmaceutical corporations, and regulators. This analytical approach assists in comprehending public viewpoints and attitudes towards drugs, therefore enabling the evaluation of the advantages and disadvantages of medications. Although neural network frameworks have made ...Show More
SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks
Graph convolutional networks (GCNs) have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborhood information. Repeatedly applying graph convolutions can cause the oversmoothing issue, i.e., node features at deep layers converge to similar values. Previous studies have suggested that oversmoothing is...Show More
Modeling Higher-Order Interactions in Graphs Through Combinatorial Arc-Transitive Structure Using Graph Convolutional Network
The analysis of networks, including social, citation, biological, and traffic networks, has become a critical research area, enabling deeper insights into complex systems across diverse fields. Traditional Graph Convolutional Networks (GCNs) have demonstrated success in graph representation learning, particularly in homophilic networks where nodes share similar features. However, these model...Show More
Learning Aligned Vertex Convolutional Networks for Graph Classification
Graph convolutional networks (GCNs) are powerful tools for graph structure data analysis. One main drawback arising in most existing GCN models is that of the oversmoothing problem, i.e., the vertex features abstracted from the existing graph convolution operation have previously tended to be indistinguishable if the GCN model has many convolutional layers (e.g., more than two layers). To ad...Show More
K-Core Based Temporal Graph Convolutional Network for Dynamic Graphs
Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core base...Show More
An Exploratory Case Study for Turkish Sentiment Classification Using Graph Convolutional Neural Networks
2021 6th International Conference on Computer Science and Engineering (UBMK)
Year: 2021 | Conference Paper |
Graph Convolutional Neural Networks (GCNs) are highly popular in recent years. It gives very successful results for various natural language processing (NLP) tasks such as sentiment classification. It has recently been shown to be effective and successful models to solve sentiment classification problem of texts. However, there is no research demonstrating the performance of this model on Tu...Show More
Enhancing Metabolomics Pathway Prediction with Sequential Graph Convolutional Network
2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT)
Year: 2025 | Conference Paper |
Metabolomics is a powerful tool for the understanding of biological systems by analysis of metabolites and their related pathways. Prediction of metabolic pathways is still one of the most challenging tasks because of the complexity of molecular structures and graph-structured metabolomics data. This article presents a robust framework using Graph Convolutional Networks (GCNs) to address the...Show More
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