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Disentangling the mechanisms underlying the social network evolution is oneof social science's unsolved puzzles. Preferential attachment is a powerfulmechanism explaining social network dynamics, yet not able to explain allscaling-laws in social networks. Recent advances in understanding socialnetwork dynamics demonstrate that several scaling-laws in social networksfollow as natural consequences of triadic closure. Macroscopic comparisonsbetween them are discussed empirically in many works. However the networkevolution drives not only the emergence of macroscopic scaling but also themicroscopic behaviors. Here we exploit two fundamental aspects of the networkmicroscopic evolution: the individual influence evolution and the process oflink formation. First we develop a novel framework for the microscopicevolution, where the mechanisms of preferential attachment and triadic closureare well balanced. Then on four real-world datasets we apply our approach fortwo microscopic problems: node's prominence prediction and link prediction,where our method yields significant predictive improvement over baselinesolutions. Finally to be rigorous and comprehensive, we further observe thatour framework has a stronger generalization capacity across different kinds ofsocial networks for two microscopic prediction problems. We unveil thesignificant factors with a greater degree of precision than has heretofore beenpossible, and shed new light on networks evolution.

Microscopic Evolution of Social Networks by Triad Position Profile