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A supervised approach to time scale detection in dynamic networks
[article]
2017
arXiv
pre-print
For any stream of time-stamped edges that form a dynamic network, an important choice is the aggregation granularity that an analyst uses to bin the data. Picking such a windowing of the data is often done by hand, or left up to the technology that is collecting the data. However, the choice can make a big difference in the properties of the dynamic network. This is the time scale detection problem. In previous work, this problem is often solved with a heuristic as an unsupervised task. As an
arXiv:1702.07752v1
fatcat:ne4sirxu4ngmfaaema4u5l6gpy