Discovering biological guilds through topological abstraction

High-throughput generation of new types of relational biological datasets is creating a demand for methods to provide insights into their complexity. Such networks are often too large to interpret visually and too complicated to be explained solely based on local topological properties. One way to try to make sense of such complex networks would be to transform them into discernable abstracts, or summaries, of the original networks. Then, important components could become more readily visible. This work presents such an approach for understanding networks via abstraction of global network connectivity using compression. This made possible the discovery of a new type of topological class, referred to herein as a guild, that captures global connectivity similarity. Lastly, the correspondence of these guilds to biological function is validated via an E. Coli gene regulation network. This resulted in biological findings that could not be derived from local topology of the original network.