Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data
This work addresses the challenge of scalable and flexible network modeling for large-scale data, offering a novel approach that improves prediction accuracy, though it is incremental in its method adaptation.
The authors tackled the problem of generative graph modeling by proposing a Laplacian spectrum-based statistic to avoid parametric assumptions and developing the Fiedler random graph model for conditional estimation, achieving higher edge prediction accuracy than alternative models on real-world networks.
Generative models for graphs have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are either only suitable for characterizing some particular network properties (such as degree distribution or clustering coefficient), or they are aimed at estimating joint probability distributions, which is often intractable in large-scale networks. In this paper, we first propose a novel network statistic, based on the Laplacian spectrum of graphs, which allows to dispense with any parametric assumption concerning the modeled network properties. Second, we use the defined statistic to develop the Fiedler random graph model, switching the focus from the estimation of joint probability distributions to a more tractable conditional estimation setting. After analyzing the dependence structure characterizing Fiedler random graphs, we evaluate them experimentally in edge prediction over several real-world networks, showing that they allow to reach a much higher prediction accuracy than various alternative statistical models.