Identifying networks with common organizational principles
This addresses the problem of network comparison for researchers in fields like biology and economics, but appears incremental as it builds on existing techniques.
The paper tackles the challenge of accurately clustering networks of different sizes and densities that are hypothesized to be structurally similar, by introducing a new network comparison methodology that identifies common organizational principles, which is simple, intuitive, and applicable in diverse settings such as protein functional classification and world trade network evolution.
Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant. There are many techniques for network comparison, from simply comparing network summary statistics to sophisticated but computationally costly alignment-based approaches. Yet it remains challenging to accurately cluster networks that are of a different size and density, but hypothesized to be structurally similar. In this paper, we address this problem by introducing a new network comparison methodology that is aimed at identifying common organizational principles in networks. The methodology is simple, intuitive and applicable in a wide variety of settings ranging from the functional classification of proteins to tracking the evolution of a world trade network.