| Full text | |
| Author(s): |
Scabini, Leonardo
;
Ribas, Lucas
;
Ribeiro, Eraldo
;
Bruno, Odemir
;
Ribeiro, P
;
Silva, F
;
Mendes, JF
;
Laureano, R
Total Authors: 8
|
| Document type: | Journal article |
| Source: | NETWORK SCIENCE (NETSCI-X 2022); v. 13197, p. 13-pg., 2022-01-01. |
| Abstract | |
The classification of complex networks allows us to compare sets of networks based on their topological characteristics. By being able to compare sets of known networks to unknown ones, we can analyze real-world complex systems such as neural pathways, traffic flow, and social relations. However, most network-classification methods rely on vertex-level measures or they characterize single fixed-structure networks. Also, these approaches can be computationally costly when analyzing a large number of networks, as they need to learn the network embeds. To address these issues, we propose a hand-crafted embedding method called Deep Topological Embedding (DTE) that builds multidimensional and deep embeddings from networks, based on the joint distribution of vertex centrality, that combined represents the global structure of the network. The DTE can be approached as a two or three-dimensional visual representation of complex networks. In this sense, we present a convolutional architecture to classify DTE representations of different topological models. Our method achieves improved classification accuracy compared to related methods when tested on three benchmarks. (AU) | |
| FAPESP's process: | 16/18809-9 - Deep learning and complex networks applied to computer vision |
| Grantee: | Odemir Martinez Bruno |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 21/09163-6 - Network science for optimizing artificial neural networks on computer vision |
| Grantee: | Leonardo Felipe dos Santos Scabini |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity |
| Grantee: | Odemir Martinez Bruno |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 19/07811-0 - Artificial neural networks and complex networks: an integrative study of topological properties and pattern recognition |
| Grantee: | Leonardo Felipe dos Santos Scabini |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 16/23763-8 - Modeling and analysis of complex networks for computer vision |
| Grantee: | Lucas Correia Ribas |
| Support Opportunities: | Scholarships in Brazil - Doctorate |