Relying heavily on machine learning algorithms for community search may introduce potential drawbacks or limitations. One limitation is the interpretability of results - complex ML models like FCS-HGNN may provide accurate predictions but lack transparency in how those predictions are made. This could make it challenging for users to understand why certain nodes are identified as part of a community. Additionally, overfitting could occur if the model is trained on limited or biased data, leading to inaccurate community detection results. Moreover, scalability could be an issue with large datasets as ML algorithms might require significant computational resources and time for training.