research-article

Graph Neural Networks for Friend Ranking in Large-scale Social Platforms

Authors:

Aravind Sankar,

Neil ShahAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 2535 - 2546

https://doi.org/10.1145/3442381.3450120

Published: 03 June 2021 Publication History

Abstract

Graph Neural Networks (GNNs) have recently enabled substantial advances in graph learning. Despite their rich representational capacity, GNNs remain under-explored for large-scale social modeling applications. One such industrially ubiquitous application is friend suggestion: recommending users other candidate users to befriend, to improve user connectivity, retention and engagement. However, modeling such user-user interactions on large-scale social platforms poses unique challenges: such graphs often have heavy-tailed degree distributions, where a significant fraction of users are inactive and have limited structural and engagement information. Moreover, users interact with different functionalities, communicate with diverse groups, and have multifaceted interaction patterns.

We study the application of GNNs for friend suggestion, providing the first investigation of GNN design for this task, to our knowledge. To leverage the rich knowledge of in-platform actions, we formulate friend suggestion as multi-faceted friend ranking with multi-modal user features and link communication features. We design a neural architecture GraFRank to learn expressive user representations from multiple feature modalities and user-user interactions. Specifically, GraFRank employs modality-specific neighbor aggregators and cross-modality attentions to learn multi-faceted user representations. We conduct experiments on two multi-million user datasets from Snapchat, a leading mobile social platform, where GraFRank outperforms several state-of-the-art approaches on candidate retrieval (by 30% MRR) and ranking (by 20% MRR) tasks. Moreover, our qualitative analysis indicates notable gains for critical populations of less-active and low-degree users.

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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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https://doi.org/10.1109/TKDE.2024.3513533
Xu JLu CJin SMeng YFu XZeng XNussinov RCheng F(2025)Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome dataNucleic Acids Research10.1093/nar/gkaf13853:5Online publication date: 4-Mar-2025
https://doi.org/10.1093/nar/gkaf138
Subramonian ASagun LSun YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Networked inequalityProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693978(46891-46925)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693978
Kung PFan ZZhao TLiu YLai ZShi JWu YYu JShah NVenkataraman GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query ExpansionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661367(2930-2934)Online publication date: 10-Jul-2024
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