NeurIPS 2020poster48 citations
Node Embeddings and Exact Low-Rank Representations of Complex Networks
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis
Abstract
Low-dimensional embeddings, from classical spectral embeddings to modern neural-net-inspired methods, are a cornerstone in the modeling and analysis of complex networks. Recent work by Seshadhri et al. (PNAS 2020) suggests that such embeddings cannot capture local structure arising in complex networks. In particular, they show that any network generated from a natural low-dimensional model cannot be both sparse and have high triangle density (high clustering coefficient), two hallmark properties of many real-world networks.
BibTeX
@inproceedings{NEURIPS2020_99503bdd,
author = {Chanpuriya, Sudhanshu and Musco, Cameron and Sotiropoulos, Konstantinos and Tsourakakis, Charalampos},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {13185--13198},
publisher = {Curran Associates, Inc.},
title = {Node Embeddings and Exact Low-Rank Representations of Complex Networks},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/99503bdd3c5a4c4671ada72d6fd81433-Paper.pdf},
volume = {33},
year = {2020}
}