Downloaded From: http://proj.ise.bgu.ac.il/sns

Academia.edu is a platform for academics to share research papers. The company's mission is to accelerate the world's research.

Academics use Academia.edu to share their research, monitor deep analytics around the impact of their research, and track the research of academics they follow. 1,103,634 academics have signed up to Academia.edu, adding 1,232,565 papers and 418,798 research interests. Academia.edu attracts over 3.1 million unique visitors a month.
Please Cite:

1. Fire, Michael, et al. "Link prediction in social networks using computationally efficient topological features." Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on. IEEE, 2011.

2. Fire, M., Tenenboim-Chekina, L., Puzis, R., Lesser, O., Rokach, L., & Elovici, Y. (2013). Computationally efficient link prediction in a variety of social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 5(1), 10.

Bibtex:

@inproceedings{fire2011link,
  title={Link prediction in social networks using computationally efficient topological features},
  author={Fire, Michael and Tenenboim, Lena and Lesser, Ofrit and Puzis, Rami and Rokach, Lior and Elovici, Yuval},
  booktitle={Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on},
  pages={73--80},
  year={2011},
  organization={IEEE}
}

@article{fire2013computationally,
  title={Computationally efficient link prediction in a variety of social networks},
  author={Fire, Michael and Tenenboim-Chekina, Lena and Puzis, Rami and Lesser, Ofrit and Rokach, Lior and Elovici, Yuval},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume={5},
  number={1},
  pages={10},
  year={2013},
  publisher={ACM}
}