Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
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Social macrostructures, such as structural balance, ranked clusters and transitivity, are of great importance on account of their abilities to reflect the underlying social psychological processes about the formation and evolution of relationships among people. Here we present a detailed study on examining the existence and evolution of social macrostructures in an empirical online social network, and exploring how they can be explained by network micro structural properties, i.e. nodal in degree and out degree and dyadic feature. We establish the micro-macro linkage by analyzing the network triadic patterns. Based on a novel clustering coefficient based network sampling approach, we show that the distribution of observed triad census in our data is low dimensional and can be greatly explained by network dyadic properties. In a time series analysis, we observe that our network exhibits strong tendencies towards balanced, transitive and clustered social macrostructure given the nodal and dyadic characteristics. Our findings supplement the studies on structural properties of online social network by providing more insights on the relation between network macrostructures and the micro-level social processes that result in them. And they form the basis to understand better how online social media systems change the information and communication fabric of our society.