There　is　a　wealth　of　information　in　real-world　social　networks.　In　addition　to　the　topology　information,　the　vertices　or　edges　of　a　social　network　often　have　attributes,　with　many　of　the　overlapping　vertices　belonging　to　several　communities　simultaneously.　It　is　challenging　to　fully　utilize　the　additional　attribute　information　to　detect　overlapping　communities.　In　this　paper,　we　first　propose　an　overlapping　community　detection　algorithm　based　on　an　augmented　attribute　graph.　An　improved　weight　adjustment　strategy　for　attributes　is　embedded　in　the　algorithm　to　help　detect　overlapping　communities　more　accurately.　Second,　we　enhance　the　algorithm　to　automatically　determine　the　number　of　communities　by　a　node-density-based　fuzzy　k-medoids　process.　Extensive　experiments　on　both　synthetic　and　real-world　datasets　demonstrate　that　the　proposed　algorithms　can　effectively　detect　overlapping　communities　with　fewer　parameters　compared　to　the　baseline　methods.