The Link Prediction Problem in Bipartite Networks

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The Link Prediction Problem in Bipartite Networks
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 function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positivesemidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document–feature networks and other types of bip...
Jérôme Kunegis, Ernesto William De Lu
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where IPMU
Authors Jérôme Kunegis, Ernesto William De Luca, Sahin Albayrak
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