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 spectru...
We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction functio...
We consider a framework for semi-supervised learning using spectral decomposition-based unsupervised kernel design. We relate this approach to previously proposed semi-supervised l...
We relate the one- and two-variable interlace polynomials of a graph to the spectra of a quadratic boolean function with respect to a strategic subset of local unitary transforms. ...