Abstract
We introduce a ranking model for temporal multidimensional weighted and directed networks based on the Perron eigenvector of a multi-homogeneous order-preserving map. The model extends to the temporal multilayer setting the HITS algorithm and defines five centrality vectors: two for the nodes, two for the layers, and one for the temporal stamps. Nonlinearity is introduced in the standard HITS model in order to guarantee existence and uniqueness of these centrality vectors for any network, without any requirement on its connectivity structure. We introduce a globally convergent power iteration like algorithm for the computation of the centrality vectors. Numerical experiments on real-world networks are performed in order to assess the effectiveness of the proposed model and showcase the performance of the accompanying algorithm.
| Language | English |
|---|---|
| Title of host publication | SIAM International Conference on Data Mining, SDM 2019 |
| Place of Publication | Philadelphia, PA |
| Pages | 369-377 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781611975673 |
| DOIs | |
| Publication status | Published - 4 May 2019 |
| Event | SIAM International Conference on Data Mining 2019 - Calgary, Canada Duration: 2 May 2019 → 4 May 2019 |
Conference
| Conference | SIAM International Conference on Data Mining 2019 |
|---|---|
| Abbreviated title | SDM19 |
| Country | Canada |
| City | Calgary |
| Period | 2/05/19 → 4/05/19 |
Fingerprint
Keywords
- perron eigenvector
- HITS
- ranking model
Cite this
}
Multi-dimensional, multilayer, nonlinear and dynamic HITS. / Arrigo, Francesca; Tudisco, Francesco.
SIAM International Conference on Data Mining, SDM 2019. Philadelphia, PA, 2019. p. 369-377.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Multi-dimensional, multilayer, nonlinear and dynamic HITS
AU - Arrigo, Francesca
AU - Tudisco, Francesco
PY - 2019/5/4
Y1 - 2019/5/4
N2 - We introduce a ranking model for temporal multidimensional weighted and directed networks based on the Perron eigenvector of a multi-homogeneous order-preserving map. The model extends to the temporal multilayer setting the HITS algorithm and defines five centrality vectors: two for the nodes, two for the layers, and one for the temporal stamps. Nonlinearity is introduced in the standard HITS model in order to guarantee existence and uniqueness of these centrality vectors for any network, without any requirement on its connectivity structure. We introduce a globally convergent power iteration like algorithm for the computation of the centrality vectors. Numerical experiments on real-world networks are performed in order to assess the effectiveness of the proposed model and showcase the performance of the accompanying algorithm.
AB - We introduce a ranking model for temporal multidimensional weighted and directed networks based on the Perron eigenvector of a multi-homogeneous order-preserving map. The model extends to the temporal multilayer setting the HITS algorithm and defines five centrality vectors: two for the nodes, two for the layers, and one for the temporal stamps. Nonlinearity is introduced in the standard HITS model in order to guarantee existence and uniqueness of these centrality vectors for any network, without any requirement on its connectivity structure. We introduce a globally convergent power iteration like algorithm for the computation of the centrality vectors. Numerical experiments on real-world networks are performed in order to assess the effectiveness of the proposed model and showcase the performance of the accompanying algorithm.
KW - perron eigenvector
KW - HITS
KW - ranking model
U2 - 10.1137/1.9781611975673.42
DO - 10.1137/1.9781611975673.42
M3 - Conference contribution book
SP - 369
EP - 377
BT - SIAM International Conference on Data Mining, SDM 2019
CY - Philadelphia, PA
ER -