Abstract: Signed social relationship prediction aims to predict whether there exists positive directed interactions or negative directed interactions between social entities in a social network. Existing social relation prediction models often overlook the signed and directional characteristics in social networks. Balance theory provides guidance for modeling signed-related social relations, while status theory offers guidance for modeling the joint signed and directional social relations. Moreover, self-supervised techniques can effectively provide mutual assistance in learning node features from these two perspectives. Accordingly, this paper proposes a self-supervised signed social relationship prediction model fusing balance and status theories, which aims to make full use of balance and status theories to model friendships and hierarchical relationships, and to capture the effects of edge signs and directions on different relationships, respectively. To improve prediction performance, a contrastive learning mechanism is used to explore the complementary information of signs and directions in the network during training. Experiments on real datasets validate the effectiveness of the proposed model in this paper. 

Key words: signed social network, balance theory, status theory, contrastive learning, relationship prediction