Semi-supervised　community　detection　has　gained　a　lot　of　attention　by　leveraging　side　information　for　better　understanding　network　topologies.　However,　most　of　existing　works　select　side　information　in　a　random　manner.　They　usually　require　a　great　amount　of　side　information　to　significantly　improve　the　performance　of　community　detection.　Besides,　they　have　to　define　the　number　of　communities　in　advance.　To　address　these　issues,　this　paper　proposed　an　active　semi-supervised　community　detection　method,　called　SK　rank　-D.　The　key　advantages　of　this　framework　are　twofold:　(1)　Actively　selecting　a　small　amount　of　links　as　side　information　to　＂sharpen＂　the　boundaries　between　communities　and　＂compact＂　the　connections　within　communities;　(2)　Automatically　referring　the　number　of　communities　by　selecting　informative　nodes　in　communities.　Empirical　analysis　on　both　synthetic　and　real-world　networks　showed　the　effectiveness　and　rationality　of　the　newly　proposed　method　in　deciding　community　number.　We　also　compared　with　competing　semi-supervised　community　detection　methods,　the　experimental　results　demonstrated　the　superior　performance　of　our　approach.　(C)　2018　Elsevier　B.V.　All　rights　reserved.