While information spread is a main effect in the world-wide social network, the actual mechanics of this process is especially hard to analyze In this talk we investigate the patterns of social influence in evolving social networks. 

 First we  analyze the fine-grained connections between the average degree and the power-law degree distribution exponent in growing information networks. Our starting observation is a power-law degree distribution with a decaying exponent and average degree as an increasing function of the size. We propose a  model that is the combination of the exponential growth of influence, and a power-law developing network, in which new ``homophily'' edges are continuously added to nodes proportional to their homophily degree. 

 Next we give methods for time-aware music recommendation in a social media service with the potential of exploiting immediate temporal influences between users. We consider events when a user listens to an artist the first time and this event follows some friend listening to the same artist short time before in the social network. We train a blend of matrix factorization methods that model the relation of the influencer, the influenced and the artist. Our experiments justify the existence of social influence besides the effect of homophily in the network.