Modeling multi-scale data via a network of networks

TL;DR

This paper introduces a network of networks (NoN) framework for multi-scale data modeling, demonstrating that NoN-based approaches improve label prediction accuracy over traditional single-level methods in synthetic and biological data.

Contribution

The paper develops the first synthetic NoN generator and applies NoN modeling to biological data, showing improved label prediction accuracy over traditional methods.

Findings

NoN approaches outperform or match single-level methods on synthetic data.

NoN approaches outperform single-level methods for nearly half of protein functions.

For 30% of functions, only NoN approaches yield meaningful predictions.

Abstract

Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher-scale) network can themselves be modeled as networks at a lower level. We argue that systems involving such entities should be integrated with a "network of networks" (NoN) representation. Then, we ask whether entity label prediction using multi-level NoN data via our proposed approaches is more accurate than using each of single-level node and graph data alone, i.e., than traditional node label prediction on the higher-level network and graph label prediction on the lower-level networks. To obtain data, we develop the first synthetic NoN generator and construct a real biological NoN. We evaluate accuracy of considered approaches when predicting artificial labels from the synthetic NoNs and proteins' functions from the biological NoN. For the synthetic NoNs, our NoN approaches outperform or are as good as node- and network-level ones depending on the NoN properties. For the biological NoN, our NoN approaches outperform the single-level approaches for just under half of the protein functions, and for 30% of the functions, only our NoN approaches make meaningful predictions, while node- and network-level ones achieve random accuracy. So, NoN-based data integration is important.