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

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.

[1]  Shao-Yuan Li,et al.  BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network , 2017, CIKM.

[2]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[3]  Le Song,et al.  Stochastic Training of Graph Convolutional Networks with Variance Reduction , 2017, ICML.

[4]  Marie-Francine Moens,et al.  User Profiling through Deep Multimodal Fusion , 2018, WSDM.

[5]  Jieping Ye,et al.  Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting , 2019, Sustainability.

[6]  Jure Leskovec,et al.  Graph Convolutional Neural Networks for Web-Scale Recommender Systems , 2018, KDD.

[7]  Jure Leskovec,et al.  Representation Learning on Graphs: Methods and Applications , 2017, IEEE Data Eng. Bull..

[8]  Joan Bruna,et al.  Community Detection with Graph Neural Networks , 2017, 1705.08415.

[9]  Samy Bengio,et al.  Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks , 2019, KDD.

[10]  Jian Pei,et al.  A Survey on Network Embedding , 2017, IEEE Transactions on Knowledge and Data Engineering.

[11]  Xiangnan He,et al.  MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video , 2019, ACM Multimedia.

[12]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[13]  Yixin Chen,et al.  Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network , 2019, ArXiv.

[14]  H. Sundaram,et al.  Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework , 2020, 2020 IEEE International Conference on Data Mining (ICDM).

[15]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[17]  Keping Yang,et al.  M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems , 2020, KDD.

[18]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[19]  Yongdong Zhang,et al.  Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs , 2017, ACM Multimedia.

[20]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[21]  Wei Zhang,et al.  GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation , 2020, SIGIR.

[22]  Yuan He,et al.  Graph Neural Networks for Social Recommendation , 2019, WWW.

[23]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[24]  Julio Gonzalo,et al.  A comparison of extrinsic clustering evaluation metrics based on formal constraints , 2009, Information Retrieval.

[25]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[26]  Hari Sundaram,et al.  A Modular Adversarial Approach to Social Recommendation , 2019, CIKM.

[27]  Max Welling,et al.  Graph Convolutional Matrix Completion , 2017, ArXiv.

[28]  Danai Koutra,et al.  TimeCrunch: Interpretable Dynamic Graph Summarization , 2015, KDD.

[29]  Dik Lun Lee,et al.  Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba , 2018, KDD.

[30]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[31]  Rossano Schifanella,et al.  Friendship prediction and homophily in social media , 2012, TWEB.

[32]  Max Welling,et al.  Variational Graph Auto-Encoders , 2016, ArXiv.

[33]  Padhraic Smyth,et al.  Prediction and ranking algorithms for event-based network data , 2005, SKDD.

[34]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[35]  Junzhou Huang,et al.  Adaptive Sampling Towards Fast Graph Representation Learning , 2018, NeurIPS.

[36]  Rajgopal Kannan,et al.  GraphSAINT: Graph Sampling Based Inductive Learning Method , 2019, ICLR.

[37]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[38]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[39]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[40]  Tina Eliassi-Rad,et al.  Generating Graph Snapshots from Streaming Edge Data , 2016, WWW.

[41]  Paul Covington,et al.  Deep Neural Networks for YouTube Recommendations , 2016, RecSys.

[42]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[43]  Yixin Chen,et al.  Weisfeiler-Lehman Neural Machine for Link Prediction , 2017, KDD.

[44]  Hari Sundaram,et al.  Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks , 2020, WWW.

[45]  Xinyang Zhang,et al.  Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction , 2020, WSDM.

[46]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[47]  Jure Leskovec,et al.  MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks , 2020, KDD.

[48]  Liang Gou,et al.  DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks , 2020, WSDM.

[49]  Yixin Chen,et al.  Link Prediction Based on Graph Neural Networks , 2018, NeurIPS.

[50]  Xianfeng Tang,et al.  Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps , 2020, KDD.