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) Comparisons on the performance of link prediction in zcNet ((a)–(c)) and pblgNet ((d)–(f)), where BMPA was compared with RA, AA, CN, and PA. For missing link prediction, a fraction of links from the network were randomly removed but the network was kept connected, while for evolving or spurious link prediction a fraction of links were randomly added on the network but with different observed networks. Each point is averaged by repeating the experiments 100 times.