  Being able to recommend links between users in online social networks is
important for users to connect with like-minded individuals as well as for the
platforms themselves and third parties leveraging social media information to
grow their business. Predictions are typically based on unsupervised or
supervised learning, often leveraging simple yet effective graph topological
information, such as the number of common neighbors. However, we argue that
richer information about personal social structure of individuals might lead to
better predictions. In this paper, we propose to leverage well-established
social cognitive theories to improve link prediction performance. According to
these theories, individuals arrange their social relationships along, on
average, five concentric circles of decreasing intimacy. We postulate that
relationships in different circles have different importance in predicting new
links. In order to validate this claim, we focus on popular feature-extraction
prediction algorithms (both unsupervised and supervised) and we extend them to
include social-circles awareness. We validate the prediction performance of
these circle-aware algorithms against several benchmarks (including their
baseline versions as well as node-embedding- and GNN-based link prediction),
leveraging two Twitter datasets comprising a community of video gamers and
generic users. We show that social-awareness generally provides significant
improvements in the prediction performance, beating also state-of-the-art
solutions like node2vec and SEAL, and without increasing the computational
complexity. Finally, we show that social-awareness can be used in place of
using a classifier (which may be costly or impractical) for targeting a
specific category of users.
