We propose a friend recommendation system (an application of link prediction) using edge embeddings on social networks. Most real-world social networks are multi-graphs, where different kinds of relationships (e.g. chat, friendship) are possible between a pair of users. Existing network embedding techniques do not leverage signals from different edge types and thus perform inadequately on link prediction in such networks. We propose a method to mine network representation that effectively exploits heterogeneity in multi-graphs. We evaluate our model on a real-world, active social network where this system is deployed for friend recommendation for millions of users. Our method outperforms various state-of-the-art baselines on Hike's social network in terms of accuracy as well as user satisfaction.
@article{arxiv.1902.03124,
title = {Heterogeneous Edge Embeddings for Friend Recommendation},
author = {Janu Verma and Srishti Gupta and Debdoot Mukherjee and Tanmoy Chakraborty},
journal= {arXiv preprint arXiv:1902.03124},
year = {2019}
}