Graph　Neural　Networks　(GNNs)　have　demonstrated　great　potential　in　achieving　outstanding　performance　in　various　graph-related　tasks,　e.g.,　graph　classification　and　link　prediction.　However,　most　of　them　suffer　from　the　following　issue:　shallow　networks　capture　very　limited　knowledge.　Prior　works　design　deep　GNNs　with　more　layers　to　solve　the　issue,　which　however　introduces　a　new　challenge,　i.e.,　the　infamous　over-smoothness.　Graph　representation　over　emphasizes　node　features　but　only　considers　the　static　graph　structure　with　a　uniform　weight　are　the　key　reasons　for　the　over-smoothness　issue.　To　alleviate　the　issue,　this　paper　proposes　a　Dynamic　Weighting　Strategy　(DWS)　for　addressing　over-smoothness.　We　first　employ　Fuzzy　C-Means　(FCM)　to　cluster　all　nodes　into　several　groups　and　get　each　node＇s　fuzzy　assignment,　based　on　which　a　novel　metric　function　is　devised　for　dynamically　adjusting　the　aggregation　weights.　This　dynamic　weighting　strategy　not　only　enables　the　intra-cluster　interactions,　but　also　inter-cluster　aggregations,　which　well　addresses　undifferentiated　aggregation　caused　by　uniform　weights.　Based　on　DWS,　we　further　design　a　Structure　Augmentation　(SA)　step　for　addressing　the　issue　of　underutilizing　the　graph　structure,　where　some　potentially　meaningful　connections　(i.e.,　edges)　are　added　to　the　original　graph　structure　via　a　parallelable　KNN　algorithm.　In　general,　the　optimized　Dynamic　Weighting　Strategy　with　Structure　Augmentation　(DWSSA)　alleviates　over-smoothness　by　reducing　noisy　aggregations　and　utilizing　topological　knowledge.　Extensive　experiments　on　eleven　homophilous　or　heterophilous　graph　benchmarks　demonstrate　the　effectiveness　of　our　proposed　method　DWSSA　in　alleviating　over-smoothness　and　enhancing　deep　GNNs　performance.　©　2024　Elsevier　Ltd