.png){kind=icon}
Return
Temporal Network Pattern Identification by Community Modelling
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-019-57123-1.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
DYNAMICS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
28.3W
Citations:
83.5W
Organization
View more organizations
Cited Papers
ShareSaveShareSaveIdentifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems.png)
PHYSICAL REVIEW XOAAI
PHYSICAL REVIEW XIF15.7
OAAIShareSaveShareSaveShareSaveShareSaveShareSave View more