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author:

Chen, Yuhong (Chen, Yuhong.) [1] | Chen, Fuhai (Chen, Fuhai.) [2] | Wu, Zhihao (Wu, Zhihao.) [3] | Chen, Zhaoliang (Chen, Zhaoliang.) [4] | Cai, Zhiling (Cai, Zhiling.) [5] | Tan, Yanchao (Tan, Yanchao.) [6] | Wang, Shiping (Wang, Shiping.) [7]

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Abstract:

Recently, heterogeneous graphs have attracted widespread attention as a powerful and practical superclass of traditional homogeneous graphs, which reflect the multi-type node entities and edge relations in the real world. Most existing methods adopt meta-path construction as the mainstream to learn long-range heterogeneous semantic messages between nodes. However, such schema constructs the node-wise correlation by connecting nodes via pre-computed fixed paths, which neglects the diversities of meta-paths on the path type and path range. In this paper, we propose a meta-path-based semantic embedding schema, which is called Heterogeneous Graph Embedding with Dual Edge Differentiation (HGE-DED) to adequately construct flexible meta-path combinations thus learning the rich and discriminative semantic of target nodes. Concretely, HGE-DED devises a Multi-Type and multi-Range Meta-Path Construction (MTR-MP Construction), which covers the comprehensive exploration of meta-path combinations from path type and path range, expressing the diversity of edges at more fine-grained scales. Moreover, HGE-DED designs the semantics and meta-path joint guidance, constructing a hierarchical short- and long-range relation adjustment, which constrains the path learning as well as minimizes the impact of edge heterophily on heterogeneous graphs. Experimental results on four benchmark datasets demonstrate the effectiveness of HGE-DED compared with state-of-the-art methods. © 2024 Elsevier Ltd

Keyword:

Contrastive Learning Generative adversarial networks Graph embeddings Graph neural networks Knowledge graph Network embeddings

Community:

  • [ 1 ] [Chen, Yuhong]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Chen, Yuhong]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350116, China
  • [ 3 ] [Chen, Fuhai]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 4 ] [Chen, Fuhai]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350116, China
  • [ 5 ] [Wu, Zhihao]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 6 ] [Wu, Zhihao]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350116, China
  • [ 7 ] [Chen, Zhaoliang]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 8 ] [Chen, Zhaoliang]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350116, China
  • [ 9 ] [Cai, Zhiling]College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou; 350002, China
  • [ 10 ] [Tan, Yanchao]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 11 ] [Tan, Yanchao]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350116, China
  • [ 12 ] [Wang, Shiping]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 13 ] [Wang, Shiping]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350116, China

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Source :

Neural Networks

ISSN: 0893-6080

Year: 2025

Volume: 183

6 . 0 0 0

JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 7

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