[Objective] This paper tries to identify the main influencing parameters of the link prediction algorithms with the help of network structures and data from multiple studies. [Methods] We retrieved empirical research on link prediction from China and abroad, which include 5 papers, 22 networks, 26 algorithms and 278 studies. We used three-level meta-analysis and Bayesian network meta-analysis to explore the network structures and their impacts on algorithms’ performance. [Results] The algorithms included in our study generally had a good predictive effect MD=1.183 2 (95%CI: (1.000 5, 1.365 9)). The network density, average degree and clustering coefficient are the main factors affecting the prediction results (Pval<0.05). Katz, LHN-II, MFI, LRW, and SRW algorithms yielded better results with sparse networks and their SUCRA values were greater than 0.5. [Limitations] Our research does not include empirical analysis with large-scale data. [Conclusions] With the help of meta-analysis, our study explores the development directions for the link prediction algorithms.
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