Graph　neural　networks　(GNNs)　have　shown　ad-vantages　in　graph-based　analysis　tasks.　However,　most　existing　methods　have　the　homogeneity　assumption　and　show　poor　performance　on　heterophilic　graphs,　where　the　linked　nodes　have　dissimilar　features　and　different　class　labels,　and　the　semantically　related　nodes　might　be　multi-hop　away.　To　address　this　limitation,　this　paper　presents　GraphRARE,　a　general　framework　built　upon　node　relative　entropy　and　deep　reinforcement　learning,　to　strengthen　the　expressive　capability　of　GNNs.　An　innovative　node　relative　entropy,　which　considers　node　features　and　structural　similarity,　is　used　to　measure　mutual　information　between　node　pairs.　In　addition,　to　avoid　the　sub-optimal　solutions　caused　by　mixing　useful　information　and　noises　of　remote　nodes,　a　deep　reinforcement　learning-based　algorithm　is　developed　to　optimize　the　graph　topology.　This　algorithm　selects　informative　nodes　and　discards　noisy　nodes　based　on　the　defined　node　relative　en-tropy.　Extensive　experiments　are　conducted　on　seven　real-world　datasets.　The　experimental　results　demonstrate　the　superiority　of　GraphRARE　in　node　classification　and　its　capability　to　optimize　the　original　graph　topology.　©　2024　IEEE.