  Networks analysis has been commonly used to study the interactions between
units of complex systems. One problem of particular interest is learning the
network's underlying connection pattern given a single and noisy instantiation.
While many methods have been proposed to address this problem in recent years,
they usually assume that the true model belongs to a known class, which is not
verifiable in most real-world applications. Consequently, network modeling
based on these methods either suffers from model misspecification or relies on
additional model selection procedures that are not well understood in theory
and can potentially be unstable in practice. To address this difficulty, we
propose a mixing strategy that leverages available arbitrary models to improve
their individual performances. The proposed method is computationally efficient
and almost tuning-free; thus, it can be used as an off-the-shelf method for
network modeling. We show that the proposed method performs equally well as the
oracle estimate when the true model is included as individual candidates. More
importantly, the method remains robust and outperforms all current estimates
even when the models are misspecified. Extensive simulation examples are used
to verify the advantage of the proposed mixing method. Evaluation of link
prediction performance on 385 real-world networks from six domains also
demonstrates the universal competitiveness of the mixing method across multiple
domains.
