Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

TL;DR

This paper introduces algorithms to reconstruct the historical evolution of networks from their current state, enabling insights into ancient biological and social networks that are otherwise inaccessible.

Contribution

It presents likelihood-based methods that reverse network growth models to uncover past network states while preserving node identities.

Findings

Algorithms accurately estimate node arrival times

Identify key anchor nodes in network growth

Reveal features of extinct networks

Abstract

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.