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Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification
Graph embedding is an advantageous technique for reducing computational costs and effectively using graph information in machine learning tasks like...
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Fake Review Detection via Heterogeneous Graph Attention Network
An approach based on a combination of semantic and non-semantic features of reviews is recognized as the most effective method for detecting fake...
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Multi-view Heterogeneous Graph Neural Networks for Node Classification
Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been...
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Graph neural architecture search with heterogeneous message-passing mechanisms
In recent years, neural network search has been utilized in designing effective heterogeneous graph neural networks (HGNN) and has achieved...
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DialGNN: Heterogeneous Graph Neural Networks for Dialogue Classification
Dialogue systems have attracted growing research interests due to its widespread applications in various domains. However, most research work focus...
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Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network
Event detection (ED) seeks to recognize event triggers and classify them into the predefined event types. Chinese ED is formulated as a...
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Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering
Incomplete multi-modal clustering (IMmC) is challenging due to the unexpected missing of some modalities in data. A key to this problem is to explore...
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Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks
In order to help investors understand the credit status of target corporations and reduce investment risks, the corporate credit rating model has...
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Multi-temporal heterogeneous graph learning with pattern-aware attention for industrial chain risk detection
Analyzing multi-channel data related to the industrial chain through graph representation learning is of significant value for industrial chain risk...
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Multimodal heterogeneous graph attention network
The real world involves many graphs and networks that are essentially heterogeneous, in which various types of relations connect multiple types of...
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Seeing both sides: context-aware heterogeneous graph matching networks for extracting-related arguments
Our research focuses on extracting exchanged views from dialogical documents through argument pair extraction (APE). The objective of this process is...
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McH-HGCN: multi-curvature hyperbolic heterogeneous graph convolutional network with type triplets
Most existing representation learning models for heterogeneous graphs depend on meta-paths, which requires domain-specific prior knowledge and...
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Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network
The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis (ABSA). However, the...
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Towards optimized scheduling and allocation of heterogeneous resource via graph-enhanced EPSO algorithm
Efficient allocation of tasks and resources is crucial for the performance of heterogeneous cloud computing platforms. To achieve harmony between...
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Self-supervised contrastive learning for heterogeneous graph based on multi-pretext tasks
With graph structure data becoming more common in practical problems, graph neural networks have shown their potential for processing graph structure...
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Graph-Enhanced Prompt Learning for Personalized Review Generation
Personalized review generation is significant for e-commerce applications, such as providing explainable recommendation and assisting the composition...
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Entity recognition based on heterogeneous graph reasoning of visual region and text candidate
Entity recognition plays a crucial role in various domains, such as natural language processing, information retrieval, and question-answering...
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Encoding feature set information in heterogeneous graph neural networks for game provenance
AbstractGame Provenance has been proposed and employed for Game Analytics tasks as they capture game session data in detail and allow exploratory...
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SR-HetGNN: session-based recommendation with heterogeneous graph neural network
The session-based recommendation system aims to predict the user’s next click based on their previous session sequence. The current studies generally...
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Heterogeneous Graph Prototypical Networks for Few-Shot Node Classification
The node classification task is one of the most significant applications in heterogeneous graph analysis, which is widely used for modeling...