NeurIPS 2018poster12 citations
Inferring Networks From Random Walk-Based Node Similarities
Jeremy Hoskins, Cameron Musco, Christopher Musco, Babis Tsourakakis
Abstract
Digital presence in the world of online social media entails significant privacy risks. In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i.e., commute times) or personalized PageRank scores. Using these similarities, the attacker seeks to infer as much information as possible about the network, including unknown pairwise node similarities and edges.
BibTeX
@inproceedings{NEURIPS2018_2f25f6e3,
author = {Hoskins, Jeremy and Musco, Cameron and Musco, Christopher and Tsourakakis, Babis},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Inferring Networks From Random Walk-Based Node Similarities},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/2f25f6e326adb93c5787175dda209ab6-Paper.pdf},
volume = {31},
year = {2018}
}