Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks

TL;DR

This paper demonstrates that incorporating social circle awareness, based on social cognitive theories, into link prediction algorithms significantly improves their accuracy in online social networks without added computational cost.

Contribution

It introduces a novel social-circles-aware approach to enhance link prediction algorithms, outperforming state-of-the-art methods like node2vec and SEAL.

Findings

Social-awareness improves link prediction accuracy.

Circle-aware algorithms outperform baseline and state-of-the-art methods.

Social-awareness can replace classifiers for user targeting.

Abstract

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.