  Detecting strong ties among users in social and information networks is a
fundamental operation that can improve performance on a multitude of
personalization and ranking tasks. Strong-tie edges are often readily obtained
from the social network as users often participate in multiple overlapping
networks via features such as following and messaging. These networks may vary
greatly in size, density and the information they carry. This setting leads to
a natural strong tie detection task: given a small set of labeled strong tie
edges, how well can one detect unlabeled strong ties in the remainder of the
network?
  This task becomes particularly daunting for the Twitter network due to scant
availability of pairwise relationship attribute data, and sparsity of strong
tie networks such as phone contacts. Given these challenges, a natural approach
is to instead use structural network features for the task, produced by {\em
combining} the strong and "weak" edges. In this work, we demonstrate via
experiments on Twitter data that using only such structural network features is
sufficient for detecting strong ties with high precision. These structural
network features are obtained from the presence and frequency of small network
motifs on combined strong and weak ties. We observe that using motifs larger
than triads alleviate sparsity problems that arise for smaller motifs, both due
to increased combinatorial possibilities as well as benefiting strongly from
searching beyond the ego network. Empirically, we observe that not all motifs
are equally useful, and need to be carefully constructed from the combined
edges in order to be effective for strong tie detection. Finally, we reinforce
our experimental findings with providing theoretical justification that
suggests why incorporating these larger sized motifs as features could lead to
increased performance in planted graph models.
