Showing 1-25 of 25,920 resultsfor "Index Terms":Graph Neural Network Model
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Graph Classification: Tradeoffs between Deep Neural Network Architecture and Graph Topology
2021 55th Asilomar Conference on Signals, Systems, and Computers
Year: 2021 | Conference Paper |
Deep learning techniques such as convolutional neural networks (CNNs) have significantly impacted fields like computer vision and other Euclidean data domains. However, many domains have non-Euclidean data, and it is of interest to extend CNNs to leverage the data graph. There has been a surge of interest in the field of geometric deep learning that adapts CNNs to graph signals. As a result, resea...Show More
Recurrent Convolution based Graph Neural Network for Node Classification in Graph structure data
2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Many datasets in various machine learning applications are structural and naturally represented as graphs. They comprise data from the analyses of social and communication networks, predictions of traffic, and fraud detection. Graphbased Deep Learning (DL) aims to construct and train graph datasets attuned models for various graph-structured based tasks. In this work, we presented a model of Graph...Show More
A Study of Internet Users’ Heterogeneity Based on Large Language Models
2024 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)
Year: 2024 | Conference Paper |
The emergence of big events is frequently followed by the existence of online public opinion, and netizens’ emotional comments heighten the hazards associated with the process of online public opinion transmission, resulting in social instability. Netizens’ emotionality is inextricably linked to their personality traits, and it is necessary to analyze their personalities in order to determine the ...Show More
Graph Neural Network-based Surrogate Models for Finite Element Analysis
Meduri Venkata Shivaditya;José Alves;Francesca Bugiotti;Frédéric Magoulès
2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time for the simulation to complete. Computational time can be prohibitive for parametric response surface exploration tasks. In this paper, we propose as an alternative, a Graph Neural Network-based graph prediction model to...Show More
Network Anomaly Detection Using a Graph Neural Network
Patrice Kisanga;Isaac Woungang;Issa Traore;Glaucio H. S. Carvalho
2023 International Conference on Computing, Networking and Communications (ICNC)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
Contrary to the many traditional network security approaches that focus on volume-based threats, the Activity and Event Network (AEN) is a new approach built on a graph model, which addresses both volumetric attacks and long-term threats that traditional security tools cannot deal with. The AEN graph structural foundation can serve as a basis to construct a graph to be used in Graph Neural Network...Show More
Molecular Insights Unveiled: A Hybrid Neural Network Model GNN-CNN for Drug Discovery
2024 International Conference on Big Data Analytics in Bioinformatics (DABCon)
Year: 2024 | Conference Paper |
The drug discovery process is well known to be lengthy and costly, making it imperative that new approaches are explored to increase the efficiency with which novel therapeutic candidates can be discovered. To the best of our knowledge, this is among the first that employed an advanced hybrid model integrating Graph Neural Networks (GNN) and Convolutional Neural Networks (CNN), in both drug reposi...Show More
Stock Type Prediction Model Based on Hierarchical Graph Neural Network
Jianhua Yao;Yuxin Dong;Jiajing Wang;Bingxing Wang;Hongye Zheng;Honglin Qin
2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)
Year: 2024 | Conference Paper |
This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph a...Show More
Enhancing The Accuracy In Food Image Recognition Using Recurrent Neural Network Model In Comparison With Graph Neural Network Model
A.Nithya Nikhil;Sai Keerthana Thalanki;Gnanajeyaraman Rajaram;B. Renganathan
2024 9th International Conference on Applying New Technology in Green Buildings (ATiGB)
Year: 2024 | Conference Paper |
To find the Accuracy in Food image recognition using Graph Neural Network Model in comparison with Recurrent Neural Network Model. Dataset of images in jpg, format consists of the images of Food items of various varieties like vegetarian and non-vegetarian. Prepare the data-set. Split the data-set into two parts: a training set and a test set. The training set will be used to train the machine lea...Show More
In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks. GNN models are usually handcrafted. However, building handcrafted GNN models is difficult and requires expert experience because GNN model components are complex and sensitive to variations. The c...Show More
Transient stability assessment of power systems with graph neural networks considering global features
Shengyuan Yang;Mengxiang Ding;Zijianga Wtuan;Heaichuang Yang;Yilin Liu;Wenli Fan
2023 IEEE 4th China International Youth Conference On Electrical Engineering (CIYCEE)
Year: 2023 | Conference Paper |
Currently, the transient stability assessment of power systems using graph neural networks often overlooks the multidimensional characteristics of transmission lines and exhibits limited utilization of overarching features. To address this issue, this paper introduces a novel framework for graph neural networks, termed Global Features-Exploiting Edge Features for Graph Convolutional Networks (G-EG...Show More
Graph Generation with Recurrent and Graph Neural Networks
2023 IEEE International Conference on Medical Artificial Intelligence (MedAI)
Year: 2023 | Conference Paper |
Graph generation has applications as diverse as drug discovery, materials design, and code completion. In this paper, we propose a novel auto-regressive graph generation model, where graph generation is viewed as a decision process. The proposed model combines the power of graph neural networks (GNNs) with generative modeling techniques, and incorporates both graph topology and node features, allo...Show More
Graph Neural Network for Digital Twin Network: A Conceptual Framework
Abubakar Isah;Hyeju Shin;Ibrahim Aliyu;Rashid Muhammad Sulaiman;Jinsul Kim
2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
Year: 2024 | Conference Paper |
Graph Neural Networks (GNNs) have emerged as a powerful framework for analyzing and extracting information from complex network data. In the realm of Digital Twin Networks (DTN), where physical entities are mirrored in a virtual environment, GNNs offer a transformative approach by leveraging the inherent structure and relationships within digital twins. GNNs enable enhanced data representation and...Show More
Predicting DNN run-times using a Graph Neural Network for heterogenous SoCs
2023 IEEE 19th International Conference on Intelligent Computer Communication and Processing (ICCP)
Year: 2023 | Conference Paper |
Deep learning models are widely used for automated driving, but achieving the goal of performance in embedded Systems-on-Chip (SoCs) has a lot of challenges due to the tradeoff between accuracy and run-time. This paper addresses the task of finding a suitable neural architecture for heterogeneous Systems-on-Chip (SoCs) in order to strike a balance between accuracy and run-time efficiency. Our appr...Show More
Graph Convolutional Neural Networks In The Companion Model
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Graph Convolutional Neural Networks (graph CNNs) adapt the traditional CNN architecture for use on graphs, replacing convolution layers with graph convolution layers. Although similar in architecture, graph CNNs are used for geometric deep learning whereas conventional CNNs are used for deep learning on grid-based data, such as audio or images, with seemingly no direct relationship between the two...Show More
Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data
Mansu Kim;Jaesik Kim;Jeffrey Qu;Heng Huang;Qi Long;Kyung-Ah Sohn;Dokyoon Kim;Li Shen
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2021 | Conference Paper |
Cited by: Papers (11)
Alzheimer’s disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimag...Show More
Research on City Traffic Flow Forecast Based on Graph Convolutional Neural Network
2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
With the continuous increase in the number of motor vehicles and the frequent occurrence of road congestion problems, it has become an important research topic to carry out comprehensive collection of traffic road network status information, processing analysis, prediction, and decision-making recommendation to effectively solve urban traffic problems. The traffic flow is one of the main parameter...Show More
Monitoring Semiconductor Residue Deposits Using Graph Neural Networks
Minho Jeon;Anil Kumar Khambampati;Keun Joong Yoon;Jong Hyun Song;Seok Jun Ko;Kyung Youn Kim
2025 International Conference on Electronics, Information, and Communication (ICEIC)
Year: 2025 | Conference Paper |
In semiconductor manufacturing processes, preventive maintenance (PM) tasks to remove toxic residues accumulated inside pipes are critical from a safety management perspective. However, performing PM work without a clear understanding of the situation inside the pipes can lead to inefficiencies in terms of cost depending on the maintenance cycle and can pose risks to the health of operators. There...Show More
Enhancing Heterophilic Graph Neural Network Performance Through Label Propagation in K-Nearest Neighbor Graphs
2024 IEEE International Conference on Big Data and Smart Computing (BigComp)
Year: 2024 | Conference Paper |
How can we exploit Label Propagation (LP) to improve the performance of GNN models on heterophilic graphs? Graph Neural Network (GNN) models have received a lot of attention as a powerful deep learning technology that uses graph structure and features, and has achieved an archived state-of-the-art performance for graph-related tasks. LP has been applied in various studies to improve performance of...Show More
Enhanced Bot Detection on TwiBot-20 Dataset
2024 9th International Conference on Computer Science and Engineering (UBMK)
Year: 2024 | Conference Paper |
Bot detection is critical in safeguarding social networks against malicious activities such as propagating misinformation and shaping public opinion. Twitter, being extensively studied due to its accessibility and interactive nature, serves as an ideal platform for studying bot behaviors. In this study, we evaluate various network configurations using TwiBot-20 dataset to assess their efficacy in ...Show More
The Graph Neural Network Model
Franco Scarselli;Marco Gori;Ah Chung Tsoi;Markus Hagenbuchner;Gabriele Monfardini
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represente...Show More
A Graph Neural Network Approach for Identification of Influencers and Micro-Influencers in a Social Network : *Classifying influencers from non-influencers using GNN and GCN
2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Targeting potential influencers for ad campaigns is one of the main challenges in social media marketing. This paper aims to incorporate Artificial Intelligence to segregate influencers from non-influencers. The target groups in focus are potential influencers. The authors build two different kinds of Graph Neural Networks, one is simple Graph Neural Network and the other is Graph Convolutional Ne...Show More
Performance Prediction in OBSS WLANs Using Machine Learning Approaches
2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI)
Year: 2023 | Conference Paper |
In high-dense deployment of WLANs with very high throughputs in environments like malls, stadiums, colleges, etc., the throughput achieved by next-generation IEEE 802.11 WLANs is much lower than expected. The estimation of throughput through simulators is cumbersome and needs elaborate information regarding the deployment details relating to overlapping basic service sets (OBSS). The objective of ...Show More
Origin-Destination (OD) traffic flow estimations from traffic sensor data play an important role for transportation planning and management. This paper proposes a novel method to compare OD traffic estimated matrices (using data from traffic sensors). The proposed method uses the estimated OD traffic flow values together with COVID-19 incidence data in order to build a sequence of temporal graphs ...Show More
GAME-BC: A Graph Attention Model for Exploring Bitcoin Crime
2022 6th International Symposium on Computer Science and Intelligent Control (ISCSIC)
Year: 2022 | Conference Paper |
Blockchain technology has been invented as a fundamental technique to the cryptocurrency Bitcoin in 2008, which is decentralized, consensus and cryptographic leger. However, due to the anonymity of the Blockchain, Bitcoin has been becoming one critical finance platform applied to transfer or hidden criminal income by offenders. Bitcoin crime refers to criminal activities which use Bitcoin as a cri...Show More
Traditional Chinese Medicine Prescription Recommendation Model Based on Large Language Models and Graph Neural Networks
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2023 | Conference Paper |
Background: Traditional Chinese medicine (TCM) has a millennia-long history, offering unique treatments and insights into global health. Given the intricate symptoms and shifting syndrome patterns, prescribing can be tough for young doctors. TCM prescription recommendations can help these doctors address their experience gap. In recent years, with advancements in technologies such as artificial in...Show More
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