Fusion Learning on Heterogeneous Social Networks

Abstract

Looking from a global perspective, the landscape of online social networks is highly fragmented. A large number of online social networks have appeared, which can provide the users with various types of services. Generally, the information available in the these online social networks is of diverse categories, which can be represented as heterogeneous information networks (HIN) formally. Meanwhile, in such an age of online social media, users usually participate in multiple online social networks simultaneously to enjoy more social networks services, who can act as bridges connecting different networks together. So multiple HINs not only represent information in single network, but also fuse information from multiple networks. Formally, the online social networks sharing common users are named as the aligned social networks, and these shared users who act like anchors aligning the networks are called the anchor users. The heterogeneous information generated by users’ social activities in the multiple aligned social networks provides social network practitioners and researchers with the opportunities to study individual user’s social behaviors across multiple social platforms simultaneously.