We propose a learning-based targeted revision (LBTR) approach for efficient incremental community detection.

We provide mathematical analysis on how the vertex classifier can affect the community detection time complexity.

Experiment results show that our approach can significantly reduce the running time while maintaining high community detection quality.

To make our approach effective, one should increase the precision of the vertex classifier while keeping recall at a reasonable level.