Link prediction in complex networks via modularity-based belief propagation<xref rid="cpb_26_3_038902_fn1" ref-type="fn">*</xref> <fn id="cpb_26_3_038902_fn1"> <label>*</label> <p>Project supported by the National Natural Science Foundation of China (Grants No. 61202262), the Natural Science Foundation of Jiangsu Province, China (Grants No. BK2012328), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grants No. 20120092120034).</p> </fn>

Link prediction in complex networks via modularity-based belief propagation* *

Project supported by the National Natural Science Foundation of China (Grants No. 61202262), the Natural Science Foundation of Jiangsu Province, China (Grants No. BK2012328), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grants No. 20120092120034).

Lai Darong^{1, 2, †}, Shu Xin³, Nardini Christine^{4, 5}

(color online) The performance of link prediction on networks with c = 10 when different numbers of blocks were used in BMPA. (a) The performance of BMPA on missing link prediction. (b) The performance of BMPA on spurious link prediction. (c) The performance of BMPA on evolving link prediction, and the inset shows the different performance with different values of q. The number q of blocks used are the real number of blocks in the networks (q = 10) and numbers around the real one (q = 8, 9, 11, and 12). Each point is averaged over 10 network instances by repeating the experiments 30 times.