The existing research on the calculating methods for WDS partitioning is mainly based on single network cases, while it lacks comparison and applicability analysis for different network cases and various requirements. This paper compares the partitioning effects of depth-first search combined with partial closeness centrality algorithm (DFS-PCC), fast iterative modularity greedy algorithm (CNM), and spectral clustering optimized by genetics algorithm (GA-SC) in five benchmark cases. The comparative analysis was achieved by developing evaluation indicators such as normalized mutual information (NMI), modularity, the balance of nodes quantity, and the number of feed lines. The influences of network intrinsic properties including topology structures of the cases, the number and types of water sources, and control elements were considered. In addition, the selection of weights and the determination of partition numbers were studied. Results show that for the cases with obvious regional water supply and high tree-like characteristics, DFS-PCC had high modularity and a large number of feed lines, while CNM had high NMI and a few feed lines. In the five cases, GA-SC had high modularity, a balanced number of nodes, and a few feed lines, indicating better applicability. By utilizing the weights of 1/q, the pipes with large flow and flow fluctuation could be effectively selected as feed lines.