Community Detection in Flow-Based Engineering Networks

Xiaoliang Wang, Fei Xue, Shaofeng Lu, Qigang Wu, Lechuan Piao, Bing Han

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Different from the conventional structural perspective to detect communities, flow-based communities are more relevant to community functions and behaviors. This paper defines and detects flow-based communities based on Coupling Strength (CS) and corresponding functional modularity to understand community structures in engineering networks. In general, CS should be defined to reflect flow transmission ability between couples of nodes by considering specific physical and control characteristics in specific engineering fields. Then, the conventional modularity could be upgraded as functional modularity based on CS. The functional modularity could be applied as a metric to evaluate any network partitioning in a corresponding engineering field. Furthermore, the Newman fast algorithm is modified with maximization of functional modularity as objective. Cohesion tightness for a specific community is proposed so that communities with different scales in different networks can be quantitatively compared. Based on studies on some real networks from different categories, it is found that flow-based community and conventional topological communities may have considerable differences in variation rule, the magnitude of modularity, and community uniformity.

Original languageEnglish
Title of host publication2023 42nd Chinese Control Conference, CCC 2023
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789887581543
Publication statusPublished - 2023
Event42nd Chinese Control Conference, CCC 2023 - Tianjin, China
Duration: 24 Jul 202326 Jul 2023

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Conference42nd Chinese Control Conference, CCC 2023


  • community detection
  • complex network
  • flow-based engineering networks


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