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作者:

Li, Mengran (Li, Mengran.) | Zhang, Yong (Zhang, Yong.) (学者:张勇) | Wang, Shaofan (Wang, Shaofan.) | Hu, Yongli (Hu, Yongli.) | Yin, Baocai (Yin, Baocai.)

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Scopus SCIE

摘要:

Attribute graphs are a crucial data structure for graph communities. However, the presence of redundancy and noise in the attribute graph can impair the aggregation effect of integrating two different heterogeneous distributions of attribute and structural features, resulting in inconsistent and distorted data that ultimately compromises the accuracy and reliability of attribute graph learning. For instance, redundant or irrelevant attributes can result in overfitting, while noisy attributes can lead to underfitting. Similarly, redundant or noisy structural features can affect the accuracy of graph representations, making it challenging to distinguish between different nodes or communities. To address these issues, we propose the embedded fusion graph auto-encoder framework for self-supervised learning (SSL), which leverages multitask learning to fuse node features across different tasks to reduce redundancy. The embedding fusion graph auto-encoder (EFGAE) framework comprises two phases: pretraining (PT) and downstream task learning (DTL). During the PT phase, EFGAE uses a graph auto-encoder (GAE) based on adversarial contrastive learning to learn structural and attribute embeddings separately and then fuses these embeddings to obtain a representation of the entire graph. During the DTL phase, we introduce an adaptive graph convolutional network (AGCN), which is applied to graph neural network (GNN) classifiers to enhance recognition for downstream tasks. The experimental results demonstrate that our approach outperforms state-of-the-art (SOTA) techniques in terms of accuracy, generalization ability, and robustness.

关键词:

Convolution self-supervised learning (SSL) Task analysis redundancy reduction Self-supervised learning Representation learning Graph neural networks attribute graphs contrastive learning Termination of employment Adaptive graph convolution Redundancy graph auto-encoder (GAE)

作者机构:

  • [ 1 ] [Li, Mengran]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Dept Informat Sci, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Yong]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Dept Informat Sci, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 3 ] [Wang, Shaofan]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Dept Informat Sci, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 4 ] [Hu, Yongli]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Dept Informat Sci, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 5 ] [Yin, Baocai]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Dept Informat Sci, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China

通讯作者信息:

  • 张勇

    [Zhang, Yong]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Dept Informat Sci, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China

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来源 :

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

ISSN: 2162-237X

年份: 2024

1 0 . 4 0 0

JCR@2022

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