Motif-Plus: incorporation of network motifs into top-n friendship recommendations

Although recently there have been a variety of attempts for friendship recommendations, most existing approaches overemphasise the use of global information implied in historical data while overlooking such essential transitivity rule in social networks as the 'triadic closure' principle. To overcome this limitation, we analyse triangular network motifs in social networks and design a novel principle named 'triangular power' for friendship recommendations on the basis of the 'triadic closure' rule. With this principle, we propose to quantify the willing-power that drives two people become friends by making use of triangular motifs and propose a novel strategy called Motif-Plus that utilise local information of a social network for friendship recommendations. We further incorporate this strategy into existing recommendation methods by aggregating ranks generated by individual approaches and Motif-Plus. With large-scale random sub-sampling validation experiments on three real social network datasets (Facebook, Twitter and Delicious), we compare the performance of five existing methods with Motif-Plus incorporated to their original forms. Results show that methods with Motif-Plus outperform their original forms in not only accuracy and retrieval criteria but also recommendation diversity significantly, suggesting that friendship recommendations will be benefit from the fusion of global and local information of social networks.