We propose a friend recommendation system (an application of link prediction) using edge embedding 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 edge 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 metrics as well as user satisfaction.
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