Due　to　the　highly　nonlinear　behavior　of　clothing,　modelling　fine-scale　garment　deformation　on　arbitrary　meshes　under　varied　conditions　within　a　unified　network　poses　a　significant　challenge.　Existing　methods　often　compromise　on　either　model　generalization,　deformation　quality,　or　runtime　speed,　making　them　less　suitable　for　real-world　applications.　To　address　it,　we　propose　to　incorporate　multi-source　graph　construction　and　pooling　to　achieve　a　novel　graph　learning　scheme.　We　first　introduce　methods　for　extracting　cues　from　different　deformation　correlations　and　transform　the　garment　mesh　into　a　comprehensive　graph　enriched　with　deformation-related　information.　To　enhance　the　learning　capability　and　generalizability　of　the　model,　we　present　structure-preserving　pooling　and　unpooling　strategies　for　the　mesh　deformation　task,　thereby　improving　information　propagation　across　the　mesh　and　enhancing　the　realism　of　deformation.　Lastly,　we　conduct　an　attribution　analysis　and　visualize　the　contribution　of　various　vertices　in　the　graph　to　the　output,　providing　insights　into　the　deformation　behavior.　The　experimental　results　demonstrate　superior　performance　against　state-of-the-art　methods.　©　2024　Association　for　Computing　Machinery.　All　rights　reserved.