With the explosion of the network data,it is difficult to effectively recognize the latent structure of the social networks by simply analyzing topology information,node attributes,and edge attributes.So,we proposed a semi-supervised community detection algorithm to give a unified respective of topology information and other attributes.First,the topology information and other attributes are taken as prior knowledge which is introduced and computed using the edge function.Then a semi-supervised framework is used to and learn the global node affiliation matrix by semi-definite programming.Experimental results on synthetic,Gannan Hakka data and benchmark network data show that our method can fully utilize all prior knowledge,the performance is better than that of many traditional algorithms and has better interpellation for detected communities.