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author:

Zhong, L. (Zhong, L..) [1] | Yang, J. (Yang, J..) [2] | Chen, Z. (Chen, Z..) [3] | Wang, S. (Wang, S..) [4]

Indexed by:

Scopus

Abstract:

Semi-supervised node classification with Graph Convolutional Network (GCN) is an attractive topic in social media analysis and applications. Recent studies show that GCN-based classification methods can facilitate the accuracy increase of learning algorithms. However, most of the existing methods do not conduct adequate explorations of the complementary information within the topology structure. Besides, they also suffer from the insufficient excavation of useful information among nodes and the scarcity of labeled samples, resulting in undesired classification performance. To cope with these issues, this paper proposes a contrastive GCN-based framework to jointly leverage the topology graph and the self-adaptive topology graph with feature information in semi-supervised information. In order to extract more valid potential information in the topology graph and increase the flexibility of the framework, we learn an adjacency matrix supervised by a flexible loss that exploits node embeddings to reinforce the topological representation capability of the adjacency matrix. To maximize the homogeneity of these two distinct graphs, we design an improved semi-supervised contrastive loss. In order to enrich scarce label information, we propose a self-supervised mechanism to generate reliable pseudo labels from abundant unlabeled data, which further refines the learnable adjacency matrix. With these modules, both unlabeled and labeled samples jointly furnish the supervision signals, thereby improving the accuracy of the proposed model. Extensive experimental results on real-world datasets demonstrate the effectiveness and superiority of the proposed algorithm against state-of-the-arts. © 1991-2012 IEEE.

Keyword:

contrastive learning generative adjacency matrix Graph convolutional networks self-supervised learning semi-supervised classification

Community:

  • [ 1 ] [Zhong, L.]Fuzhou University, College of Computer and Data Science, Fujian, Fuzhou, 350116, China
  • [ 2 ] [Zhong, L.]Fuzhou University, Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fujian, Fuzhou, 350116, China
  • [ 3 ] [Yang, J.]Fuzhou University, College of Computer and Data Science, Fujian, Fuzhou, 350116, China
  • [ 4 ] [Yang, J.]Fuzhou University, Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fujian, Fuzhou, 350116, China
  • [ 5 ] [Chen, Z.]Fuzhou University, College of Computer and Data Science, Fujian, Fuzhou, 350116, China
  • [ 6 ] [Chen, Z.]Fuzhou University, Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fujian, Fuzhou, 350116, China
  • [ 7 ] [Wang, S.]Fuzhou University, College of Computer and Data Science, Fujian, Fuzhou, 350116, China
  • [ 8 ] [Wang, S.]Fuzhou University, Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fujian, Fuzhou, 350116, China

Reprint 's Address:

  • [Wang, S.]Fuzhou University, Fujian, China

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Source :

IEEE Transactions on Signal Processing

ISSN: 1053-587X

Year: 2023

Volume: 71

Page: 772-785

4 . 6

JCR@2023

4 . 6 0 0

JCR@2023

ESI HC Threshold:35

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 11

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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