Name
Abstract | ||
|---|---|---|
Learning graph Laplacian matrices plays a crucial role in network analytics when a meaningful graph is not readily available from the datasets. However, graph Laplacian inference is an ill-posed problem, since multiple solutions may exist to associate a graph with the data. Recent papers have exploited signal smoothness or graph sparsity to handle this problem, without considering specific graph t... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSIPN.2019.2936361 | IEEE Transactions on Signal and Information Processing over Networks |
Keywords | Field | DocType |
Laplace equations,Estimation,Information processing,Signal processing,Network topology,Symmetric matrices,Topology | Laplacian matrix,Signal processing,Community structure,Information processing,Inference,Computer science,Symmetric matrix,Network topology,Theoretical computer science,Topological property | Journal |
Volume | Issue | ISSN |
5 | 4 | 2373-776X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanli Yuan | 1 | 0 | 0.68 |
| De Wen Soh | 2 | 0 | 0.34 |
| howard hua yang | 3 | 216 | 32.06 |
| Tony Q. S. Quek | 4 | 3621 | 276.75 |