| Full text | |
| Author(s): |
Gao, Xubo
[1]
;
Zheng, Qiusheng
[1]
;
Vega-Oliveros, Didier A.
[2, 3]
;
Anghinoni, Leandro
[4]
;
Zhao, Liang
[3]
Total Authors: 5
|
| Affiliation: | [1] Zhongyuan Univ Technol, Sch Comp Sci, Henan Key Lab Publ Opin Intelligent Anal, Zhengzhou - Peoples R China
[2] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN - USA
[3] Univ Sao Paulo, Fac Philosophy Sci & Letters Ribeirao Preto FFCLR, Ribeirao Preto, SP - Brazil
[4] Univ Sao Paulo, Inst Math & Comp Sci ICMC USP, Sao Carlos, SP - Brazil
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | SCIENTIFIC REPORTS; v. 10, n. 1 JAN 14 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Temporal network mining tasks are usually hard problems. This is because we need to face not only a large amount of data but also its non-stationary nature. In this paper, we propose a method for temporal network pattern representation and pattern change detection following the reductionist approach. The main idea is to model each stable (durable) state of a given temporal network as a community in a sampled static network and the temporal state change is represented by the transition from one community to another. For this purpose, a reduced static single-layer network, called a target network, is constructed by sampling and rearranging the original temporal network. Our approach provides a general way not only for temporal networks but also for data stream mining in topological space. Simulation results on artificial and real temporal networks show that the proposed method can group different temporal states into different communities with a very reduced amount of sampled nodes. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 18/24260-5 - Spatiotemporal Data Analytics based on Complex Networks |
| Grantee: | Didier Augusto Vega Oliveros |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| FAPESP's process: | 16/23698-1 - Dynamical Processes in Complex Network based on Machine Learning |
| Grantee: | Didier Augusto Vega Oliveros |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 15/50122-0 - Dynamic phenomena in complex networks: basics and applications |
| Grantee: | Elbert Einstein Nehrer Macau |
| Support Opportunities: | Research Projects - Thematic Grants |