MIERank: Co-ranking Individuals and Communities with Multiple Interactions in Evolving Networks

Ranking has significant applications in real life. It aims to evaluate the importance (or popularity) of two categories of objects, i.e., individuals and communities. Numerous efforts have been dedicated to these two types of rankings respectively. Instead, in this paper, we for the first time explore the co-ranking of both individuals and communities. Our insight lies in that co-ranking may enhance the mutual evaluation on both sides. To this end, we first establish an Evolving Coupled Graph that contains a series of smoothly weighted snapshots, each of which characterizes and couples the intricate interactions of both individuals and communities till a certain evolution time into a single graph. Then we propose an algorithm, called MIERank to implement the co-ranking of individuals and communities in the proposed evolving graph. The core idea of MIERank lies in a novel unbiased random walk, which, when sampling the interplay among nodes over different generation times, incorporates the preference knowledge of ranking by utilizing nodes' future actions. MIERank returns the co-ranking of both individuals and communities by iteratively alternating between their corresponding stationary probabilities of the unbiased random walk in a mutually-reinforcing manner. We prove the efficiency of MIERank in terms of its convergence, optimality and extensiblity. Our experiments on a big scholarly dataset of 606862 papers and 1215 fields further validate the superiority of MIERank with fast convergence and an up to 26% ranking accuracy gain compared with the separate counterparts.