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.　Copyright　©2021　Journal　of　Harbin　Institute　of　Technology.All　rights　reserved.