.png){kind=icon}
.png){kind=icon}
Return
A matrix factorization approach to graph compression with partial information
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-014-0286-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We address the problem of encoding a graph of order n into a graph of order k < n in a way to minimize reconstruction error. This encoding is characterized in terms of a particular factorization of the adjacency matrix of the original graph. The factorization is determined as the solution of a discrete optimization problem, which is for convenience relaxed into a continuous, but equivalent, one. Our formulation does not require to have the full graph, but it can factorize the graph also in the presence of partial information. We propose a multiplicative update rule for the optimization task resembling the ones introduced for nonnegative matrix factorization, and convergence properties are proven. Experiments are conducted to assess the effectiveness of the proposed approach.
Keywords:
Matrix factorization
Graph compression
Stochastic blockmodel
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.7
Papers:
3.1K
Citations:
5.6K