Complex　networks　in　real　world　are　always　in　the　state　of　evolution　and　composed　of　numerous　overlapping　communities.　The　discovery　of　overlapping　communities　in　dynamic　networks　plays　an　important　role　in　community　detection　research.　In　recent　years,　methods　based　on　incremental　clustering　have　become　increasingly　popular　owing　to　their　high　efficiency.　However,　few　of　them　can　deal　with　communities　that　are　both　overlapping　and　dynamic.　In　this　article,　we　propose　an　incremental　clustering　algorithm　for　discovering　overlapping　communities　in　dynamic　networks.　In　the　initial　snapshot　of　a　dynamic　network,　a　degree-based　seed　selection　strategy　with　concise　and　effective　rules　is　employed　to　obtain　stable　and　high-quality　overlapping　communities,　in　which　the　degree　of　nodes　is　the　number　of　their　neighboring　nodes　in　the　subgraph　composed　of　free　nodes.　In　the　subsequent　snapshots,　a　four-staged　framework　based　on　cascade　information　diffusion　is　proposed　to　update　the　communities　incrementally.　In　this　framework,　a　cascade　information　diffusion　model　is　used　to　simulate　the　evolution　of　communities　and　then　the　fitness　of　nodes　to　the　communities　they　belong　to　is　updated　based　on　node　similarity.　Experiments　conducted　on　both　real-world　and　artificial　datasets　show　that　the　proposed　algorithm　can　discover　overlapping　communities　in　dynamic　networks　effectively　and　outperform　to　the　state-of-art　baseline　algorithms.