LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs
Abstract
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.
Cite
Text
Liu et al. "LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9082Markdown
[Liu et al. "LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/liu2014aaai-lsdh/) doi:10.1609/AAAI.V28I1.9082BibTeX
@inproceedings{liu2014aaai-lsdh,
title = {{LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs}},
author = {Liu, Dawei and Wang, Yuanzhuo and Jia, Yantao and Li, Jingyuan and Yu, Zhihua},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {3120-3121},
doi = {10.1609/AAAI.V28I1.9082},
url = {https://mlanthology.org/aaai/2014/liu2014aaai-lsdh/}
}