Effective Fault Scenario Identification for Communication Networks Via Knowledge-Enhanced Graph Neural Networks

Haihong ZHAO, Bo YANG, Jiaxu CUI, Qianli XING, Jiaxing SHEN, Fujin ZHU, Jiannong CAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)


Fault Scenario Identification (FSI) is a challenging task that aims to automatically identify the fault types in communication networks from massive alarms to guarantee effective fault recoveries. Existing methods are developed based on rules, which are not accurate enough due to the mismatching issue. In this paper, we propose an effective method named Knowledge-Enhanced Graph Neural Network (KE-GNN), the main idea of which is to integrate the advantages of both the rules and GNN. This work is the first work that employs GNN and rules to tackle the FSI task. Specifically, we encode knowledge using propositional logic and map them into a knowledge space. Then, we elaborately design a teacher-student scheme to minimize the distance between the knowledge embedding and the prediction of GNN, integrating knowledge and enhancing the GNN. To validate the performance of the proposed method, we collected and labeled three real-world 5 G fault scenario datasets. Extensive evaluation conducted on these datasets indicates that our method achieves the best performance compared with other representative methods, improving the accuracy by up to 8.10%. Furthermore, the proposed method achieves the best performance against a small dataset setting and can be effectively applied to a new carrier site with a different topology structure.
Original languageEnglish
Pages (from-to)3243-3258
Number of pages16
JournalIEEE Transactions on Mobile Computing
Issue number4
Early online date1 May 2023
Publication statusPublished - Apr 2024

Bibliographical note

Publisher Copyright:
© 2002-2012 IEEE.


  • Communication networks
  • Fault diagnosis
  • Graph neural networks
  • Knowledge engineering
  • Network topology
  • Task analysis
  • Topology
  • fault scenario identification
  • graph neural network
  • knowledge
  • propositional logic


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