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Learning to solve graph metric dimension problem based on graph contrastive learning
Deep learning has been widely used to solve graph and combinatorial optimization problems. However, proper model deployment is critical for training...
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Improving graph-based recommendation with unraveled graph learning
Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph...
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Multi-behavior-based graph contrastive learning recommendation
Graph-based collaborative filtering recommendations can more effectively explore the interaction information between users and items. However, its...
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Simple knowledge graph completion model based on PU learning and prompt learning
Knowledge graphs (KGs) are important resources for many artificial intelligence tasks but usually suffer from incompleteness, which has prompted...
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Learning graph-based representations for scene flow estimation
Scene flow estimation is a fundamental task of autonomous driving. Compared with optical flow, scene flow can provide sufficient 3D motion...
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GNNCL: A Graph Neural Network Recommendation Model Based on Contrastive Learning
In the field of recommendation algorithms, the representation learning for users and items has evolved from using single IDs or historical...
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DC-Graph: a chunk optimization model based on document classification and graph learning
Existing machine reading comprehension methods use a fixed stride to chunk long texts, which leads to missing contextual information at the...
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Graph-based comparative analysis of learning to rank datasets
The relative success of learning to rank algorithms has raised the attention of the research community for develo** efficient and effective ranking...
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BotCL: a social bot detection model based on graph contrastive learning
The proliferation of social bots on social networks presents significant challenges to network security due to their malicious activities. While...
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Graph neural news recommendation based on multi-view representation learning
Accurate news representation is of crucial importance in personalized news recommendation. Most of existing news recommendation model lack...
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A heterogeneous graph-based semi-supervised learning framework for access control decision-making
For modern information systems, robust access control mechanisms are vital in safeguarding data integrity and ensuring the entire system’s security....
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Multi-view clustering based on graph learning and view diversity learning
Multi-view clustering is to make full use of different views of the data for clustering. In recent years, many multi-view clustering methods have...
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Graph learning-based generation of abstractions for reinforcement learning
The application of reinforcement learning (RL) algorithms is often hindered by the combinatorial explosion of the state space. Previous works have...
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Learning on heterogeneous graph neural networks with consistency-based augmentation
Heterogeneous Graph Neural Networks(HGNNs), as an effective tool for mining heterogeneous graphs, have achieved remarkable performance on series of...
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Graph Contrastive Learning with Constrained Graph Data Augmentation
Studies on graph contrastive learning, which is an effective way of self-supervision, have achieved excellent experimental performance. Most existing...
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Task-related network based on meta-learning for few-shot knowledge graph completion
Knowledge graph (KG) is a powerful tool in many areas, but it is impossible to take in all knowledge during construction for the complexity of...
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Large-scale knowledge graph representation learning
The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks....
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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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Cardinality estimation for property graph queries with gated learning approach on the graph database
With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the...
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Consensus Affinity Graph Learning via Structure Graph Fusion and Block Diagonal Representation for Multiview Clustering
Learning a robust affinity graph is fundamental to graph-based clustering methods. However, some existing affinity graph learning methods have...
