A　clear　game　process　helps　to　track　community　generation　and　evolution,　so　non-cooperative　and　cooperative　games　are　applied　to　community　detection.　However,　non-cooperative　games　focus　on　the　competition　between　nodes,　disregarding　their　cooperation.　Solely　considering　individual　perspectives　often　results　in　insufficient　precision.　Cooperative　games　consider　the　interests　of　both　coalitions　and　individual.　Nevertheless,　involving　a　large　number　of　participants　in　cooperative　games　can　lead　to　high　computational　complexity　and　slow　convergence.　In　this　study,　a　fast　community　detection　model　called　FCDG　is　proposed.　It　combines　non-cooperative　and　cooperative　games　by　exploring　candidate　communities　and　optimizing　community　merging.　Firstly,　a　intimate　core　group　identification　strategy　based　on　node　mutual　intimacy　is　designed　to　accelerate　the　convergence　of　candidate　community　detection　using　non-cooperative　games　and　maximize　individual　benefits.　Secondly,　building　upon　of　candidate　community,　a　candidate　community　merging　approach　based　on　cooperative　games　is　devised　to　achieve　community　optimal　solution.　The　performance　of　FCDG　is　evaluated　on　both　real-world　and　synthetic　datasets.　Experimental　results　demonstrate　that　FCDG　effectively　discovers　community　structure　with　higher　accuracy　and　robustness　compared　to　other　baseline　algorithms.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.