  Multidimensional scaling in networks allows for the discovery of latent
information about their structure by embedding nodes in some feature space.
Ideological scaling for users in social networks such as Twitter is an example,
but similar settings can include diverse applications in other networks and
even media platforms or e-commerce. A growing literature of ideology scaling
methods in social networks restricts the scaling procedure to nodes that
provide interpretability of the feature space: on Twitter, it is common to
consider the sub-network of parliamentarians and their followers. This allows
to interpret inferred latent features as indices for ideology-related concepts
inspecting the position of members of parliament. While effective in inferring
meaningful features, this is generally restrained to these sub-networks,
limiting interesting applications such as country-wide measurement of
polarization and its evolution. We propose two methods to propagate ideological
features beyond these sub-networks: one based on homophily (linked users have
similar ideology), and the other on structural similarity (nodes with similar
neighborhoods have similar ideologies). In our methods, we leverage the concept
of neighborhood ideological coherence as a parameter for propagation. Using
Twitter data, we produce an ideological scaling for 370K users, and analyze the
two families of propagation methods on a population of 6.5M users. We find
that, when coherence is considered, the ideology of a user is better estimated
from those with similar neighborhoods, than from their immediate neighbors.
