The　existence　of　communities　in　various　complex　networks　is　ubiquitous　in　all　aspects　of　people’s　living.　Hence,　it　is　crucial　to　uncover　communities　accurately,　which　is　one　of　the　hottest　research　areas　in　the　field　of　network　analysis.　Particularly,　complex　networks　are　usually　in　continuous　change　so　that　it　is　more　realistic　to　uncover　dynamic　communities.　In　this　study,　an　algorithm　based　on　node　contribution　for　uncovering　dynamic　communities　is　proposed.　Firstly,　the　seed　nodes　are　selected　via　node　local　fitness　in　the　network,　thus　guaranteeing　that　the　selected　seeds　are　central　nodes　of　communities.　Secondly,　a　static　algorithm　is　used　to　obtain　communities　in　initial　snapshot　of　the　network.　Finally,　node　contribution　is　proposed　to　incrementally　uncover　communities　in　non-initial　snapshots　of　the　network.　The　experimental　results　reveal　that　our　method　outperforms　all　other　comparison　algorithms　in　both　artificial　and　real　datasets.　©　2019,　Springer　Nature　Singapore　Pte　Ltd.