Learning spectral graph transformations for link prediction

11 years 7 months ago
Learning spectral graph transformations for link prediction
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.
Andreas Lommatzsch, Jérôme Kunegis
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Andreas Lommatzsch, Jérôme Kunegis
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