High-order graph attention network

Article Computer Science, Information Systems

Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation

Xiaofei Zhu et al.

Summary: This paper proposes a novel method called DGS-MGNN, which utilizes a dynamic global structure enhanced multi-channel graph neural network to learn accurate representations of items. The method dynamically generates local, global, and consensus graphs using a multi-channel graph neural network, and learns more informative item representations based on the corresponding graph. In addition, the graph structure is used to assist the attention mechanism in filtering noisy information within sessions, resulting in an accurate intention representation for the user. Finally, a more accurate prediction probability distribution is generated by combining a repeat and explore module.

INFORMATION SCIENCES (2023)

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Article Computer Science, Information Systems

AIC-GNN: Adversarial information completion for graph neural networks

Quanmin Wei et al.

Summary: This paper proposes a pluggable framework called Adversarial Information Completion Graph Neural Networks (AIC-GNN) to address the problem of low-degree node representation learning. A novel Graph Information Generator is introduced to adaptively fit the node missing information distribution, and adversarial training is used to enhance the representational capacity of the model. Extensive experiments demonstrate the superior performance of AIC-GNN compared to state-of-the-art methods on four real-world graphs.

INFORMATION SCIENCES (2023)

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Article Computer Science, Information Systems

The identical distribution hypothesis is equivalent to the parameter discrepancy hypothesis: Adversarial attacks on graph neural networks

Yiteng Wu et al.

Summary: This paper addresses the vulnerability of graph neural networks to adversarial attacks and proposes an attack model based on the identical distribution hypothesis to address the issue of distribution consistency between training and test sets. The paper also analyzes the problem of graph poisoning attacks, derives the influence of perturbations in the training set on the test set, and delimits the feasible region of the attack. Numerical examples and experimental results validate the correctness of the attack gradient and the reasonableness and effectiveness of the proposed method.

INFORMATION SCIENCES (2023)

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Article Computer Science, Information Systems

SocialLGN: Light graph convolution network for social recommendation

Jie Liao et al.

Summary: This paper proposes a new social recommendation system called SocialLGN, which addresses the challenges of accurately learning user and item representations from user-item interaction graphs and social graphs, as well as integrating user representations learned from these two graphs. Experimental results demonstrate the superiority of SocialLGN, especially in handling the cold-start problem.

INFORMATION SCIENCES (2022)

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Article Computer Science, Information Systems

Adaptive graph convolutional collaboration networks for semi-supervised classification

Sichao Fu et al.

Summary: This paper proposes adaptive graph convolutional collaboration networks (AGCCNs) for semi-supervised classification of non-Euclidean data. AGCCNs utilize semantic information from different convolution layers and an attention mechanism to learn robust deep semantic features, addressing issues in traditional GCNs and achieving superior results compared to traditional GCNs in experimental evaluations.

INFORMATION SCIENCES (2022)

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Article Computer Science, Information Systems

Influence maximization in social networks using graph embedding and graph neural network

Sanjay Kumar et al.

Summary: With the boom in technologies and mobile networks, online social networks have become integral and influential in our daily lives. This paper proposes a novel method of influence maximization using graph embedding and graph neural networks, which outperforms previous methods according to experimental results on real-life networks.

INFORMATION SCIENCES (2022)

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Article Computer Science, Information Systems

Semi-supervised learning with mixed-order graph convolutional networks

Jie Wang et al.

Summary: The proposed mixed-order graph convolutional networks (MOGCN) address the oversmoothing issue and underutilization of pseudo-labels of unlabeled nodes in semi-supervised learning. MOGCN consists of two modules: constructing multiple GCN learners with multi-order adjacency matrices and employing a novel ensemble module to efficiently combine results from these learners. Experimental results on three public benchmark datasets demonstrate that MOGCN consistently outperforms state-of-the-art methods.

INFORMATION SCIENCES (2021)

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Article Computer Science, Artificial Intelligence