  In this work we give a community detection algorithm in which the communities
both respects the intrinsic order of a directed acyclic graph and also finds
similar nodes. We take inspiration from classic similarity measures of
bibliometrics, used to assess how similar two publications are, based on their
relative citation patterns. We study the algorithm's performance and antichain
properties in artificial models and in real networks, such as citation graphs
and food webs. We show how well this partitioning algorithm distinguishes and
groups together nodes of the same origin (in a citation network, the origin is
a topic or a research field). We make the comparison between our partitioning
algorithm and standard hierarchical layering tools as well as community
detection methods. We show that our algorithm produces different communities
from standard layering algorithms.

Physics and Society;Social and Information Networks