BGU Social Networks Security Research Group

 
www.academia.edu

Academia Dataset(Directed)

 

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:

  • Fire, M., Tenenboim-Chekina, L., Puzis, R., Lesser, O.,Rokach, L., and Elovici, Y., Link Prediction in Social Networks using Computationally Efficient Topological Features IEEE Third International Confernece on Social Computing (SocialCom), 2011.
  • Fire, M., Tenenboim-Chekina, L., Puzis, R., Lesser, O., Rokach, L., and Elovici, Y. "Computationally efficient link prediction in a variety of social networks." ACM Transactions on Intelligent Systems and Technology (TIST) 5.1 (2013): 10.
Bibtex:
@inproceedings{

title={Link Prediction in Social Networks using Computationally Efficient Topological Features},
author={Fire, M. and Tenenboim, L. and Lesser, O. and Puzis, R. and Rokach, L. and Elovici, Y.},
booktitle={ IEEE Third International Confernece on Social Computing (SocialCom)},
pages={73--80},
year={2011},
organization={IEEE}
}

@article{

title={Computationally Efficient Link Prediction in Variety of Social Networks},
author={Fire, M., and Tenenboim, R., and Puzis, R., and Lesser, O., and Rokach, L., and Elovici, Y. },
journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
volume={5},
number={1},
pages={10},
year={2013},
publisher={ACM}
}

Nodes Number: Edge Number:
200,169 1,398,063
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