Towards a Framework for Learning from Networked Data

TL;DR: This paper focuses on large networks of data where everything is connected directly or indirectly to everything else and the increase in complexity has a very deep effect on the theory the authors use to reason about the data.

Abstract: Over the past decades, one has seen databases of ever increasing size and complexity. While the increasing size is easy to measure in bytes, kilobytes or terabytes, the increase in complexity is more difficult to quantify, however, it has a very deep effect on the theory we use to reason about the data. While in earlier days many researchers reasoned in terms of sets of similarly structured and independent objects, today we are facing large networks of data where everything is connected directly or indirectly to everything else. Examples include social networks, traffic networks, biological networks, administrative networks and economic networks