Multi-feature fusion friend recommendation algorithm based on complex network Online publication date: Tue, 14-Nov-2023
by Kan Pan; Hailong Chen; Qian Liu; Jian Wang; Yingming Pu; Chunlin Yin; Zheng Yang; Na Zhao
International Journal of Information and Communication Technology (IJICT), Vol. 23, No. 4, 2023
Abstract: At present, one of the problems of friend recommendation algorithms used in most social networks is that these networks often rely on a single index for recommendation. To solve this problem, multi-feature fusion (MFF) algorithm, a social network friend recommendation algorithm based on complex network theory, is proposed. The recommendation algorithm works by firstly divides the existing social networks into different communities. The importance of nodes in a social network is then calculated through the fusion of nodes' importance information. Lastly, by integrating node importance information, friend number information and the shortest path information features are comprehensively evaluated, so as to generate final friend recommendation list. Simulation shows that with the increase of network nodes, the MFF algorithm outperforms common friend (CF) algorithm and friend similarity (FS) algorithm over all evaluation indicators including P-value, R-value and F1-value.
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