Behaviorally　similar　neighbors　in　the　interaction　graph　have　been　actively　explored　to　facilitate　the　collaboration　between　users　and　items　and　address　the　interaction　sparsity　issue.　We　investigate　homogenous　neighbors　between　users　or　items　to　mine　collaborative　signals　for　embedding　learning.　In　the　case　of　multiple　and　complex　composition　of　user　interests　and　item　attributes,　traditional　uniform　embedding　is　insufficient　to　depict　matching　between　a　specific　user-item　pair.　Therefore,　we　propose　a　siamese　graph-based　dynamic　matching　(SGDM)　model　for　collaborative　filtering.　A　target-aware　dual　attention　module　is　introduced　to　update　neighbors　with　their　varying　contributions　to　unveil　users’　interests,　which　explicitly　encodes　the　clue　of　the　candidate　matching　target.　For　a　target　user-item　pair,　homogeneous　neighbors　participate　in　the　interest　propagation　through　graph　convolution,　which　learns　user/item　embeddings　respectively　on　the　siamese　homogeneous　graphs.　The　dual　dynamic　aggregation　in　graph　convolution　endows　a　specific　user-item　pair　with　dynamic　matching,　which　is　expected　to　meet　the　needs　of　fine-grained　filtering　and　promote　the　performance　of　collaborative　filtering.　Extensive　experimental　results　confirm　the　collaboration　between　siamese　homogeneous　graphs　of　users　and　items.　It　further　illustrates　the　effectiveness　of　the　proposed　SGDM　in　mining　homogeneous　collaborative　signals　for　embedding　learning　and　collaborative　filtering.　©　2022　Elsevier　Inc.