Name
Abstract | ||
|---|---|---|
The matrix exponential has been identified as a useful tool for the analysis of undirected networks, with sound theoretical justifications for its ability to model important aspects of a given network. Its use for directed networks, however, is less developed and has been less successful so far. In this article we discuss some methods to identify important nodes in a directed network using the matrix exponential, taking into account that the notion of importance changes whether we consider the influence of a given node along the edge directions (downstream influence) or how it is influenced by directed paths that point to it (upstream influence). In addition, we introduce a family of importance measures based on counting walks that are allowed to reverse their direction a limited number of times, thus capturing relationships arising from influencing the same nodes, or being influenced by the same nodes, without sacrificing information about edge direction. These measures provide information about branch points. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cam.2019.01.015 | Journal of Computational and Applied Mathematics |
Keywords | Field | DocType |
Network analysis,Matrix exponential,Linear algebra | Mathematical optimization,Matrix exponential,Mathematics,Branch point | Journal |
Volume | ISSN | Citations |
355 | 0377-0427 | 1 |
PageRank | References | Authors |
0.37 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omar De la Cruz Cabrera | 1 | 3 | 1.09 |
| Mona Matar | 2 | 3 | 0.75 |
| Lothar Reichel | 3 | 453 | 95.02 |