PREDICTING TRIADIC CLOSURE IN NETWORKS USING COMMUNICABILITY DISTANCE FUNCTIONS.

We propose a communication-driven mechanism for predicting triadic closure in complex networks. It is mathematically formulated on the basis of communicability distance functions that account for the quality of communication between nodes in the network. We study 25 real-world networks and show that the proposed method correctly predicts 20% of triadic closures in these networks, in contrast to the 7.6% predicted by a random mechanism. We also show that the communication-driven method outperforms the random mechanism in explaining the clustering coefficient, average path length, and average communicability. The new method also displays some interesting features with regards to optimizing communication in networks. [ABSTRACT FROM AUTHOR]