Adaptive User Distance Modeling in Social Media

One important challenge in social network analysis is how to model users’ distance as a single measure. We propose to model this distance by simultaneously exploring users’ profile attributes and local network structures. Due to the sparsity of data, where each user may interact with just a few people and only a few users provide their profile information, it is typically difficult to learn effective distance measures for any individual network. One important observation is that, people nowadays engage in multiple social networks, such as Facebook, Twitter, etc., where auxiliary knowledge from related networks can help alleviate the data sparsity problem. Nonetheless, due to the network differences, borrowing knowledge directly does not work well. Instead, we propose an adaptive metric learning framework. The basic idea is to exploit knowledge from related networks collectively through embedding and employ boosting-based techniques to eliminate irrelevant attributes. We evaluate the adaptive user distance measure on link prediction problem an important social modeling task. Empirical studies demonstrate that the proposed approach significantly improves the link-prediction precision over state-of-theart metric learning and link prediction approaches on two large-scale social networking datasets significantly.