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. There are a variety of ways a tie can be deemed "strong", and in this work we use a data-driven (or supervised) approach by assuming that we are provided a sample set of edges labeled as strong ties in the network. Such labeled 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 -for instance, a heavily-used dense network (such as the network of followers) commonly overlaps with a secondary sparser network composed of strong ties (such as a network of email or phone contacts). 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 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.
However, Rotabi et al. REF use multiple overlapping networks to solve the strong tie detection task.
Detecting Strong Ties Using Network Motifs
ArXiv
ArXiv
Computer Science Physics