Inherent Limits on Topology-Based Link Prediction
Abstract
Link prediction systems (e.g. recommender systems) typically use graph topology as one of their main sources of information. However, automorphisms and related properties of graphs beget inherent limits in predictability. We calculate hard upper bounds on how well graph topology alone enables link prediction for a wide variety of real-world graphs. We find that in the sparsest of these graphs the upper bounds are surprisingly low, thereby demonstrating that prediction systems on sparse graph data are inherently limited and require information in addition to the graph topology.
Cite
Text
Hibshman and Weninger. "Inherent Limits on Topology-Based Link Prediction." Transactions on Machine Learning Research, 2023.Markdown
[Hibshman and Weninger. "Inherent Limits on Topology-Based Link Prediction." Transactions on Machine Learning Research, 2023.](https://mlanthology.org/tmlr/2023/hibshman2023tmlr-inherent/)BibTeX
@article{hibshman2023tmlr-inherent,
title = {{Inherent Limits on Topology-Based Link Prediction}},
author = {Hibshman, Justus Isaiah and Weninger, Tim},
journal = {Transactions on Machine Learning Research},
year = {2023},
url = {https://mlanthology.org/tmlr/2023/hibshman2023tmlr-inherent/}
}