DMCA
Learning Spectral Graph Transformations for Link Prediction
| Citations: | 48 - 5 self |
BibTeX
@MISC{Kunegis_learningspectral,
author = {Jérôme Kunegis and Andreas Lommatzsch},
title = {Learning Spectral Graph Transformations for Link Prediction},
year = {}
}
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Abstract
We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph’s algebraic spectrum. Our approach generalizes several graph kernels and dimensionality reduction methods and provides a method to estimate their parameters efficiently. We show how the parameters of these prediction functions can be learned by reducing the problem to a one-dimensional regression problem whose runtime only depends on the method’s reduced rank and that can be inspected visually. We derive variants that apply to undirected, weighted, unweighted, unipartite and bipartite graphs. We evaluate our method experimentally using examples from social networks, collaborative filtering, trust networks, citation networks, authorship graphs and hyperlink networks. 1.
Keyphrases
link prediction spectral graph transformation citation network several graph kernel derive variant hyperlink network dimensionality reduction method graph algebraic spectrum trust network unified framework social network edge weight prediction function large network authorship graph prediction function bipartite graph one-dimensional regression problem collaborative filtering
