Name
Abstract | ||
|---|---|---|
Abstract The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting—and prevalent in several fields of study—problem is that of inferring a function defined over the nodes of a network . This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complemented with a set of numerical examples, which showcase the effectiveness of the presented techniques, as well as their merits related to state-of-the-art methods. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Machine Learning | Graph kernel,Radial basis function kernel,Kernel embedding of distributions,Inference,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,String kernel,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1711.10353 | 0 |
PageRank | References | Authors |
0.34 | 27 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassilis N. Ioannidis | 1 | 14 | 7.34 |
| Meng Ma | 2 | 0 | 0.34 |
| Athanasios N. Nikolakopoulos | 3 | 59 | 9.02 |
| G. B. Giannakis | 4 | 11464 | 1206.47 |