At present, academic social network platforms have problems such as information overload and information asymmetry, which makes it difficult for scholars, especially those with low influence, to find contents they are interested in. At the same time, the scholars with high influence in the academic social network promote the formation of academic community and guide the scientific research of the scholars with low influence. Therefore, an Influential Scholar Recommendation Model based on Academic Community Detection (ISRMACD) was proposed to provide recommendation service for the scholars with low influence in academic social networks. First, the influential scholar group was used as the core structure of community to detect the academic community in complex network topological relationship generated by the relationship bonding — friendship among the scholars in the academic social network. Then the influences of scholars in the academic social network were calculated, and the recommendation service of influential scholars in the community was implemented. Experimental results on SCHOLAT dataset show that the proposed model achieves high recommendation quality under different influential scholar recommendation numbers, and has the best recommendation accuracy obtained by recommending 10 influential scholars each time, reaching 70% and above.

An academic paper recommendation model based on community partition was proposed according to sociability in social network. The model regarded the largest connected component in complex network as the logic unit in data processing, and divided up the largest connected component into non-intersect kernel sub-network. The labels would be established according to kernel sub-network by non-parameter control mode. Communities were divided in scholar social network through label propagation, and academic papers were recommended among the users in the communities by the results of the community partition. The proposed community partition method was compared with the classic community partition method in the experiments on artificial network. The experimental results show that the proposed method can achieve good community partition qualities on different characteristic artificial networks.

Concerning the problem that community detection algorithm based on label propagation in complex networks has a pre-parameter limit in the real network and redundant labels, a community detection model in large scale academic social networks was proposed. The model detected Utmost Maximal Cliques (UMC) in the academic social network and arbitrary intersection between the UMC is the empty set, and then let nodes of each UMC share the unique label by reducing redundant labels and random factors, so the model increased the efficiency and stability of the algorithm. Meanwhile the model completed label propagation of the UMC adjacent nodes using closeness from core node groups (UMC) to spread around, Non-UMC adjacent nodes in the network were updated according to the maximum weight of its neighbor nodes. In the post-processing stage an adaptive threshold method removed useless labels, thereby effectively overcame the pre-parameter limitations in the real complex network. The experimental results on academic social networking platform-SCHOLAT data set prove that the model has an ability to assign nodes with certain generality to the same community, and it provides support of the academic social networks precise personalized service in the future, such as latent friend recommendation and paper sharing.