Showing 1-25 of 37,371 resultsfor "Index Terms":Graph neural networks
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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
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
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
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
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
Graph Neural Networks for Z-DNA prediction in Genomes
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Deep learning methods have been successfully applied to the tasks of predicting functional genomic elements such as histone marks, transcriptions factor binding sites, non-B DNA structures, and regulatory variants. Initially convolutional neural networks (CNN) and recurrent neural networks (RNN) or hybrid CNN-RNN models appeared to be the methods of choice for genomic studies. With the advance of ...Show More
Classification of graph topologies by machine learning methods
Veniamin S. Bobakov;Egor A. Koryukin;Anastasia S. Butorova;Aleksandr P. Sergeev
2023 7th Scientific School Dynamics of Complex Networks and their Applications (DCNA)
Year: 2023 | Conference Paper |
Many objects of the real world, both living and man-made, may be described in the terms of graph theory and represented as graph data. One of the tasks of graph data analysis is to classify graph topologies. This work explored the possibilities of machine learning methods, and in particular graph neural networks (GNNs), to classify graphs with different topologies. The aim was to study the robustn...Show More
Computational Capabilities of Graph Neural Networks
Franco Scarselli;Marco Gori;Ah Chung Tsoi;Markus Hagenbuchner;Gabriele Monfardini
In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G, n) isin R m that maps a graph G and one of its nodes n onto an m-dimension...Show More
Residual convolutional graph neural network with subgraph attention pooling
Tsinghua Science and Technology
Cited by: Papers (15)
The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational complexity. However, pooling shrinkage discards graph details, and existing pooling methods may lead to the loss of key classification features. In this work, we propose a residual convolutional graph neural network to tackle the problem of key classification f...Show More
DAG-GCN: Directed Acyclic Causal Graph Discovery from Real World Data using Graph Convolutional Networks
2023 IEEE International Conference on Big Data and Smart Computing (BigComp)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Causal discovery has been challenging since the search space of directed acyclic graphs super-exponentially grows with respect to the number of nodes. Previously constraint-based and score-based methods have been used. In recent studies, a continuous optimization method has reached a high score, but the problem is still harsh in real-world observational data. Motivated by the success of recent GNN...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
Semi-Supervised Label Propagation Community Detection on Graphs with Graph Neural Network
2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)
Year: 2022 | Conference Paper |
The Graph Neural Network is a fairly innovative concept which permits neural networks to function on random graphs. As unbounded problem structures are universal in real-world scenarios and can be best denoted by graphs, Graph Neural Networks suggests new exhilarating applications and further simplified latent for machine learning wholly, but also noteworthy enhancement of performance in a deep le...Show More
Research on Social Network Influence Maximization Algorithm Based on Graph Neural Network
2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE)
Year: 2024 | Conference Paper |
In this paper, we propose an innovative Graph Neural Network (GNN) model that combines PageRank, genetic algorithm, and Graph Convolutional neural Network (GCN) to solve the problem of influence maximization in social networks. By utilizing the PageRank algorithm to pre-rank the nodes, we obtain the potential key influencers in the network. Subsequently, genetic algorithm is introduced to optimize...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
Graph Neural Networks for Graph Drawing
Graph drawing techniques have been developed in the last few years with the purpose of producing esthetically pleasing node-link layouts. Recently, the employment of differentiable loss functions has paved the road to the massive usage of gradient descent and related optimization algorithms. In this article, we propose a novel framework for the development of Graph Neural Drawers (GNDs), machines ...Show More
In the last decade, player and ball tracking data have increasingly been gathered and employed in various team sports, in particular invasion team sports, such as soccer, basketball, rugby, and American football. Additionally, the effectiveness of geometric analysis of formations using such tracking data has been demonstrated. Moreover, deep neural network-based methods for analyzing such tracking...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
A Survey on Graph Neural Networks Method under Social Recommendation
2023 9th Annual International Conference on Network and Information Systems for Computers (ICNISC)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
With the exponential growth of online data, the social recommendation system conducts a comprehensive analysis of user-item interaction and user-user social relationships to understand the user’s interests and generate personalized recommendations. The integration of graph neural networks and social recommendation is a current hot topic in academia. Based on the survey, we reviewed the social reco...Show More
Estimation of Graph Parameters Using Graph Convolutional Neural Networks
2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS)
Year: 2024 | Conference Paper |
The paper addresses the potential of deep learning neural network architectures in estimating parameters of a graph. We considered two parameters namely complementary distance pattern uniform (CDPU) number and Kappa. A graph $G =(V, E)$ is a complementary distance pattern uniform (CDPU), if there exists $M\subset V(G)$ such that $f_{M}(u)=\{d(u, v)$: $v\in M\}$, for every $u\in V(G)-M$, is indepen...Show More
Attention Based Graph Neural Networks
2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The performance of graph neural networks (GNNs) in a variety of graph-related tasks, such as node categorization, has been remarkably good. Existing GNN models, especially when working with big and sparse graphs, are constrained in how well they can capture complicated graph topologies. In order to overcome this issue, we incorporate an attention mechanism into the GNN design in this study. During...Show More
Multistructure Graph Classification Method With Attention-Based Pooling
Yuhua Xu;Junli Wang;Mingjian Guang;Chungang Yan;Changjun Jiang
Graph neural networks (GNNs) have achieved effective performance in many graph-related tasks involving recommendation systems, social networks, and bioinformatics. Recent studies have proposed several graph pooling operators to obtain graph-level representations from node representations. Nevertheless, they usually adopt a single strategy to evaluate the importance of nodes, which may generate nod...Show More
Graph neural networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutional filters. Otherwise, GNNs operate as CNNs. Filters are composed of pointwise nonlinearities and sta...Show More
Representing data in array form is not always an efficient way. Sometimes it can cause loss of bias information that is inherent in data. With the help of taking an image to data matrix form as input instead of flattening to an array, deep learning methods, especially convolutional neural networks, are more successful than traditional machine learning techniques in terms of accuracy rate in image ...Show More
Graph Neural Network Model Based on Layered Converter Aggregation
2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)
Year: 2023 | Conference Paper |
Graph data is an important data in real society. With the development of neural network technology, graph neural networks are receiving more and more attention. However, the existing methods mostly use adjacency matrix or attention to aggregate the information of surrounding nodes, or use random walk to select neighboring nodes for aggregation, but this aggregation method is too simple and ignores...Show More
Attention-based Graph Neural Network Enabled Method to Predict Short-term Metro Passenger Flow
Lin Li;Jun Xu;S. Thomas Ng;Jiajian Zhang;Shenghua Zhou;Yifan Yang
2020 5th International Conference on Universal Village (UV)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Effective, accurate, and reliable prediction of short-term metro passenger flow is essential to improving the operational efficiency and passenger travel experience of public transport, as well as enhancing the stakeholder emergency response capability against adverse events. Various deep learning models like the long short-term memory (LSTM) models and the graph convolutional network (GCN) have b...Show More
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