1 code implementation • 7 Oct 2021 • Thomas Gebhart, Jakob Hansen, Paul Schrater
Knowledge graph embedding involves learning representations of entities -- the vertices of the graph -- and relations -- the edges of the graph -- such that the resulting representations encode the known factual information represented by the knowledge graph and can be used in the inference of new relations.
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Jakob Hansen, Thomas Gebhart
We present a generalization of graph convolutional networks by generalizing the diffusion operation underlying this class of graph neural networks.
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Hans Riess, Jakob Hansen, Robert Ghrist
Multiparameter persistent homology has been largely neglected as an input to machine learning algorithms.
1 code implementation • 4 Aug 2018 • Jakob Hansen, Robert Ghrist
This paper outlines a program in what one might call spectral sheaf theory --- an extension of spectral graph theory to cellular sheaves.
Algebraic Topology Combinatorics 55N30, 05C50
no code implementations • 15 Aug 2016 • Jared Culbertson, Dan P. Guralnik, Jakob Hansen, Peter F. Stiller
We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli.