  Community detection, which focuses on clustering vertex interactions, plays a
significant role in network analysis. However, it also faces numerous
challenges like missing data and adversarial attack. How to further improve the
performance and robustness of community detection for real-world networks has
raised great concerns. In this paper, we explore robust community detection by
enhancing network structure, with two generic algorithms presented: one is
named robust community detection via genetic algorithm (RobustECD-GA), in which
the modularity and the number of clusters are combined in a fitness function to
find the optimal structure enhancement scheme; the other is called robust
community detection via similarity ensemble (RobustECD-SE), integrating
multiple information of community structures captured by various vertex
similarities, which scales well on large-scale networks. Comprehensive
experiments on real-world networks demonstrate, by comparing with two
traditional enhancement strategies, that the new methods help six
representative community detection algorithms achieve more significant
performance improvement. Moreover, experiments on the corresponding adversarial
networks indicate that the new methods could also optimize the network
structure to a certain extent, achieving stronger robustness against
adversarial attack. The source code of this paper is released on
https://github.com/jjzhou012/RobustECD.
