Learning the mechanisms of network growth
We propose a novel model-selection method for dynamic real-life networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.
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Datasets
Introduced in the Paper:
Synthetic Dynamic Networks
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Graph Classification | Synthetic Dynamic Networks | Time-cohort Dynamic Features + Static Features | Accuracy | 98.4 | # 1 | |
| Graph Classification | Synthetic Dynamic Networks | Static Features | Accuracy | 92.81% | # 3 | |
| Graph Classification | Synthetic Dynamic Networks | Size-cohort Dynamic Features + Static Features | Accuracy | 98.06 | # 2 |
