Unsupervised Learning via Network-Aware Embeddings
Anne Sophie Riis Damstrup Sofie Tosti Madsen Michele Coscia
Data clustering, the task of grouping observations according to their
similarity, is a key component of unsupervised learning -- with real world
applications in diverse fields such as biology, medicine, and social science.
Often in these fields the data comes with complex interdependencies between the
dimensions of analysis, for instance the various characteristics and opinions
people can have live on a complex social network. Current clustering methods
are ill-suited to tackle this complexity: deep learning can approximate these
dependencies, but not take their explicit map as the input of the analysis. In
this paper, we aim at fixing this blind spot in the unsupervised learning
literature. We can create network-aware embeddings by estimating the network
distance between numeric node attributes via the generalized Euclidean
distance. Differently from all methods in the literature that we know of, we do
not cluster the nodes of the network, but rather its node attributes. In our
experiments we show that having these network embeddings is always beneficial
for the learning task; that our method scales to large networks; and that we
can actually provide actionable insights in applications in a variety of fields
such as marketing, economics, and political science. Our method is fully open
source and data and code are available to reproduce all results in the paper.