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Networks analysis has been commonly used to study the interactions betweenunits of complex systems. One problem of particular interest is learning thenetwork'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 notverifiable in most real-world applications. Consequently, network modelingbased on these methods either suffers from model misspecification or relies onadditional model selection procedures that are not well understood in theoryand can potentially be unstable in practice. To address this difficulty, wepropose a mixing strategy that leverages available arbitrary models to improvetheir individual performances. The proposed method is computationally efficientand almost tuning-free; thus, it can be used as an off-the-shelf method fornetwork modeling. We show that the proposed method performs equally well as theoracle estimate when the true model is included as individual candidates. Moreimportantly, the method remains robust and outperforms all current estimateseven when the models are misspecified. Extensive simulation examples are usedto verify the advantage of the proposed mixing method. Evaluation of linkprediction performance on 385 real-world networks from six domains alsodemonstrates the universal competitiveness of the mixing method across multipledomains.

Network Estimation by Mixing: Adaptivity and More