Universal Inference for model selection on networks

Model selection and hypothesis testing are important tasks on networks.

A key challenge lies in the inherent dependence in network data, as well as the fact that typically only a single realization is observed.

As a result, many existing methods must be carefully tailored to specific models and only come with asymptotic theoretical guarantees.

In this work, however, we propose a general model selection framework using Universal Inference, making our method widely applicable to various testing scenarios.

Since Universal Inference requires two sets of data, we employ edge sampling to obtain proper networks with tractable dependence.

We prove that the proposed statistic is an e-value, thus controlling the type I error rate in finite samples under nearly any hypothesis test.

To our knowledge, this is the first Universal Inference-type statistic constructed from dependent splits of data as well as the first finite-sample testing guarantee for hypothesis testing on networks.

We also prove that the logarithm of the test statistic diverges to positive infinity under various alternative models.

On simulated and real-world networks, the proposed method performs well on tasks such as choosing the random graph model and the number of communities.