In　this　paper,　we　consider　the　balanced　2-correlation　clustering　problem　on　well-proportional　graphs,　which　has　applications　in　protein　interaction　networks,　cross-lingual　link　detection,　communication　networks,　among　many　others.　Given　a　complete　graph　G=(V,E)　with　each　edge　(u,v)\in　E　labeled　by　+　or　−,　the　goal　is　to　partition　the　vertices　into　two　clusters　of　equal　size　to　minimize　the　number　of　positive　edges　whose　endpoints　lie　in　different　clusters　plus　the　number　of　negative　edges　whose　endpoints　lie　in　the　same　cluster.　We　provide　a　(Formula　Presented)-balanced　approximation　algorithm　for　the　balanced　2-correlation　clustering　problem　on　M-proportional　graphs.　Namely,　the　cost　of　the　vertex　partition　(Formula　Presented)　returned　by　the　algorithm　is　at　most　(Formula　Presented)　times　the　optimum　solution,　and　(Formula　Presented).　©　2020,　Springer　Nature　Switzerland　AG.