Name
Abstract | ||
|---|---|---|
We present a novel algorithm for learning the spectral density of large scale networks using stochastic trace estimation and the method of maximum entropy. The complexity of the algorithm is linear in the number of non-zero elements of the matrix, offering a computational advantage over other algorithms. We apply our algorithm to the problem of community detection in large networks. We show state-of-the-art performance on both synthetic and real datasets. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Mathematical optimization,Large networks,Matrix (mathematics),Algorithm,Spectral density,Principle of maximum entropy,Mathematics |
DocType | Volume | Citations |
Journal | abs/1804.06802 | 0 |
PageRank | References | Authors |
0.34 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diego Granziol | 1 | 6 | 2.15 |
| Bin Xin Ru | 2 | 1 | 4.42 |
| Stefan Zohren | 3 | 1 | 2.08 |
| Xiaowen Dong | 4 | 249 | 22.07 |
| Michael Osborne | 5 | 250 | 33.49 |
| stephen j roberts | 6 | 1244 | 174.70 |