Name
Abstract | ||
|---|---|---|
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 positive-semidefinite 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 bipartite networks. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-14049-5_39 | information processing and management of uncertainty |
Keywords | DocType | Volume |
general graph,bipartite network,authorship graph,link prediction problem,bipartite case,new link prediction,bipartite graph adjacency vertex,general link prediction algorithm,link prediction function,common link prediction function,bipartite graph | Conference | abs/1006.5367 |
ISBN | Citations | PageRank |
3-642-14048-3 | 30 | 1.54 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérôme Kunegis | 1 | 874 | 51.20 |
| Ernesto W. De Luca | 2 | 50 | 3.71 |
| sahin albayrak | 3 | 1298 | 158.51 |