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The concept information of graph granule with application to knowledge graph embedding
Knowledge graph embedding (KGE) has become one of the most effective methods for the numerical representation of entities and their relations in...
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Time-varying graph learning from smooth and stationary graph signals with hidden nodes
Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches...
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Multi-DGI: Multi-head Pooling Deep Graph Infomax for Human Activity Recognition
Human Activity Recognition (HAR) is a crucial research domain with substantial real-world implications. Despite the extensive application of machine...
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An MMSE graph spectral magnitude estimator for speech signals residing on an undirected multiple graph
The paper uses the K -graphs learning method to construct weighted, connected, undirected multiple graphs, aiming to reveal intrinsic relationships of...
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Traffic Flow Forecasting of Graph Convolutional Network Based on Spatio-Temporal Attention Mechanism
Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation. Due to the complex time and space...
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Modeling multiple latent information graph structures via graph convolutional network for aspect-based sentiment analysis
Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of aspects in a sentence. Recently, graph convolution network (GCN)...
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Kane’s equations for nonholonomic systems in bond-graph-compatible velocity and momentum forms
The authors’ previously published results on a bond-graph-compatible and nonholonomic momentum form of Kane’s equations are extended, from...
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Bert-based graph unlinked embedding for sentiment analysis
Numerous graph neural network (GNN) models have been used for sentiment analysis in recent years. Nevertheless, addressing the issue of...
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Robust graph neural networks with Dirichlet regularization and residual connection
Graph Neural Network (GNN) has attracted considerable research interest in various graph data modeling tasks. Most GNNs require efficient and...
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A self-attention dynamic graph convolution network model for traffic flow prediction
Precise and reliable traffic predictions play a vital role in contemporary traffic management, particularly within complex traffic networks....
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Integrating knowledge representation into traffic prediction: a spatial–temporal graph neural network with adaptive fusion features
Various external factors that interfere with traffic flow, such as weather conditions, traffic accidents, incidents, and Points of Interest (POIs),...
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Speech emotion recognition based on Graph-LSTM neural network
Currently, Graph Neural Networks have been extended to the field of speech signal processing. It is the more compact and flexible way to represent...
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Prediction and control of fracture paths in disordered architected materials using graph neural networks
Architected materials typically rely on regular periodic patterns to achieve improved mechanical properties such as stiffness or fracture toughness....
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Sequence-Aware Graph Neural Network Incorporating Neighborhood Information for Session-Based Recommendation
Session-based recommendation is an important part of many e-commerce websites. Its purpose is to make recommendations based on the interaction...
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Traffic Flow Forecasting Based on Transformer with Diffusion Graph Attention Network
Because of the high nonlinearity and complexity, it is still a challenge to forecast traffic flow accurately. Most of the existing methods, which...
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STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks
With the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much...
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Experimental Analysis of the Multidimensional-Matrix Approach to Construct Routes in a Graph
Abstract —The algorithms for calculating the weights of routes between all pairs of graph vertices have polynomial computational complexity. However,...
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Intelligent Conceptual Design of Railway Bridge Based on Graph Neural Networks
In the conceptual design stage of railway bridge, the beam type of the bridge at the main control point must be modified repeatedly to satisfy...
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Knowledge graph completion model based on hyperbolic hierarchical attention network
Knowledge graph completion (KGC) infers missing knowledge triples based on the facts in the knowledge base. In recent years, many representation...
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DeepSIM: a novel deep learning method for graph similarity computation
AbstractGraphs are widely used to model real-life information, where graph similarity computation is one of the most significant applications, such...
