Abstract: Link prediction is of particular interest to the data mining and machine learning communities. Until recently all approaches to the problem used embedding-based methods which leverage either node similarities or latent group memberships towards link prediction. Chen and Zhang recently developed a class of non-embedding approaches called Weisfeiler-Leman (WL) Models. WL-Models extract subgraphs around links and then encode subgraph patterns via adjacency matrices using the so-called Palette-WL algorithm. A training stage then learns nonlinear graph topological features for link prediction. Chen and Zhang compared two WL-Models – a linear regression model (“WLLR”) and a neural networks model (“WLNM”) – against 12 different common link prediction schemes. In this paper, all author claims are validated for WLLR. Additionally, WLLR is tested against 22 additional embedding-based link prediction techniques arising from common neighbor-, path- and random walk-based schemes. WLLR is shown not to be superior when calculable. In fact, in 80% of the datasets where comparisons were possible, one of our added implementations proved superior.

Katie Brodhead, “Link Prediction Schemes Contra Weisfeiler-Leman Models” International Journal of Advanced Computer Science and Applications(IJACSA), 9(6), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090603

@article{Brodhead2018,
title = {Link Prediction Schemes Contra Weisfeiler-Leman Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090603},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090603},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {6},
author = {Katie Brodhead}
}