LaFT-tree: perceiving the expansion trace of one's circle of friends in online social networks

Many patterns have been discovered to explain and analyze how people make friends. Among them is the triadic closure, supported by the principle of the transitivity of friendship, which means for an individual the friends of her friend are more likely to become her new friends. However, people's motivations under this principle haven't been well studied, and it's still unknown that how this principle works in diverse situations. In this paper, we try to study this principle deeply based on the behavior modeling. We study how one expands her egocentric network via her friends, also called intermediaries, based on the transitivity of friendship. We propose LaFT-Tree, a tree-based representation of friendship formation inspired from triadic closure. LaFT-Tree provides a hierarchical view of the flat structure of one's egocentric network by visualizing the expansion trace of one's egocentric network. We model people's friend-making behaviors using LaFT-LDA, a generative model for LaFT-Tree learning. The proposed model is evaluated on both synthetic and real-world social networks and experimental results demonstrate the effectiveness of LaFT-LDA for LaFT-Tree inference. We also present some interesting applications of the LaFT-Tree, showing that our model can be generalized and benefit other social network analysis tasks.