LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs


  • Dawei Liu Chinese Academy of Sciences
  • Yuanzhuo Wang Chinese Academy of Sciences
  • Yantao Jia Chinese Academy of Sciences
  • Jingyuan Li Chinese Academy of Sciences
  • Zhihua Yu Chinese Academy of Sciences



link prediction, locality-sensitive hashing, social distance


One challenge of link prediction in online social networks is the large scale of many such networks. The measures used by existing work lack a computational consideration in the large scale setting. We propose the notion of social distance in a multi-dimensional form to measure the closeness among a group of people in Microblogs. We proposed a fast hashing approach called Locality-sensitive Social Distance Hashing (LSDH), which works in an unsupervised setup and performs approximate near neighbor search without high-dimensional distance computation. Experiments were applied over a Twitter dataset and the preliminary results testified the effectiveness of LSDH in predicting the likelihood of future associations between people.




How to Cite

Liu, D., Wang, Y., Jia, Y., Li, J., & Yu, Z. (2014). LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).