skip to main content
10.1145/3488560.3498398acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
research-article
Public Access

Friend Story Ranking with Edge-Contextual Local Graph Convolutions

Published: 15 February 2022 Publication History

Abstract

Social platforms have paved the way in creating new, modern ways for users to communicate with each other. In recent years, multiple platforms have introduced ''Stories'' features, which enable broadcasting of ephemeral multimedia content. Specifically, ''Friend Stories,'' or Stories meant to be consumed by one's close friends, are a popular feature, promoting significant user-user interactions by allowing people to see (visually) what their friends and family are up to. A key challenge in surfacing Friend Stories for a given user, is in ranking over each viewing user's friends to efficiently prioritize and route limited user attention. In this work, we explore the novel problem of Friend Story Ranking from a graph representation learning perspective. More generally, our problem is a link ranking task, where inferences are made over existing links (relations), unlike common node or graph-based tasks, or link prediction tasks, where the goal is to make inferences about non-existing links. We propose ELR, an edge-contextual approach which carefully considers local graph structure, differences between local edge types and directionality, and rich edge attributes, building on the backbone of graph convolutions. ELR handles social sparsity challenges by considering and attending over neighboring nodes, and incorporating multiple edge types in local surrounding egonet structures. We validate ELR on two large country-level datasets with millions of users and tens of millions of links from Snapchat. ELR shows superior performance over alternatives by 8% and 5% error reduction measured by MSE and MAE correspondingly. Further generality, data efficiency and ablation experiments confirm the advantages of ELR.

References

[1]
Sami Abu-El-Haija, Bryan Perozzi, and Rami Al-Rfou. 2017. Learning edge representations via low-rank asymmetric projections. In KDD . 1787--1796.
[2]
Leman Akoglu, Mary McGlohon, and Christos Faloutsos. 2010. Oddball: Spotting anomalies in weighted graphs. In PAKDD. Springer, 410--421.
[3]
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. JSM, Vol. 2008, 10 (2008), P10008.
[4]
Cécile Bothorel, Juan David Cruz, Matteo Magnani, and Barbora Micenkova. 2015. Clustering attributed graphs: models, measures and methods. arXiv preprint arXiv:1501.01676 (2015).
[5]
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013).
[6]
Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2019. Social attentional memory network: Modeling aspect-and friend-level differences in recommendation. In WSDM. 177--185.
[7]
Inderjit S Dhillon. 2001. Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. 269--274.
[8]
Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD . 135--144.
[9]
Dhivya Eswaran, Christos Faloutsos, Sudipto Guha, and Nina Mishra. 2018. Spotlight: Detecting anomalies in streaming graphs. In KDD. 1378--1386.
[10]
Martin Everett and Stephen P Borgatti. 2005. Ego network betweenness. Social networks, Vol. 27, 1 (2005), 31--38.
[11]
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In TheWebConf .
[12]
Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, and Ying Ding. 2019. edge2vec: Learning node representation using edge semantics . Technical Report.
[13]
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In ICML. PMLR.
[14]
Liyu Gong and Qiang Cheng. 2019. Exploiting edge features for graph neural networks. In CVPR. 9211--9219.
[15]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. 855--864.
[16]
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017a. Inductive Representation Learning on Large Graphs. In NIPS .
[17]
William L Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017).
[18]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173--182.
[19]
Hong Huang, Jie Tang, Sen Wu, Lu Liu, and Xiaoming Fu. 2014. Mining triadic closure patterns in social networks. In WWW. 499--504.
[20]
Meng Jiang, Peng Cui, Rui Liu, Qiang Yang, Fei Wang, Wenwu Zhu, and Shiqiang Yang. 2012. Social contextual recommendation. In CIKM. 45--54.
[21]
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020. Graph structure learning for robust graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 66--74.
[22]
Parisa Kaghazgaran, Maarten Bos, Leonardo Neves, and Neil Shah. 2020. Social Factors in Closed-Network Content Consumption. In CIKM. 595--604.
[23]
Hyun J Kim and James B Stiff. 1991. Social networks and the development of close relationships. Human Communication Research, Vol. 18, 1 (1991), 70--91.
[24]
Thomas N Kipf and Max Welling. 2016a. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[25]
Thomas N Kipf and Max Welling. 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016).
[26]
Alec Kirkley, George T Cantwell, and MEJ Newman. 2019. Balance in signed networks. Physical Review E, Vol. 99, 1 (2019), 012320.
[27]
Danai Koutra, U Kang, Jilles Vreeken, and Christos Faloutsos. 2014. Vog: Summarizing and understanding large graphs. In SDM. SIAM, 91--99.
[28]
Yi-Yu Lai and Jennifer Neville. 2020. MERL: Multi-View Edge Representation Learning in Social Networks. In CIKM . 675--684.
[29]
Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Signed networks in social media. In CHI. 1361--1370.
[30]
Zhao Li, Zhanlin Liu, Jiaming Huang, Geyu Tang, Yucong Duan, Zhiqiang Zhang, and Yifan Yang. 2019. MV-GCN: multi-view graph convolutional networks for link prediction. IEEE Access, Vol. 7 (2019), 176317--176328.
[31]
Weiping Liu and Linyuan Lü. 2010. Link prediction based on local random walk. EPL, Vol. 89, 5 (2010), 58007.
[32]
Yozen Liu, Xiaolin Shi, Lucas Pierce, and Xiang Ren. 2019. Characterizing and forecasting user engagement with in-app action graph: A case study of snapchat. In KDD. 2023--2031.
[33]
Tiancheng Lou, Jie Tang, John Hopcroft, Zhanpeng Fang, and Xiaowen Ding. 2013. Learning to predict reciprocity and triadic closure in social networks. TKDD, Vol. 7, 2 (2013), 1--25.
[34]
Linyuan Lü and Tao Zhou. 2011. Link prediction in complex networks: A survey. Physica A, Vol. 390, 6 (2011), 1150--1170.
[35]
Hao Ma, Haixuan Yang, Michael R Lyu, and Irwin King. 2008. Sorec: social recommendation using probabilistic matrix factorization. In CIKM . 931--940.
[36]
Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, and Neil Shah. 2020. A unified view on graph neural networks as graph signal denoising. arXiv preprint arXiv:2010.01777 (2020).
[37]
Yao Ma, Suhang Wang, Charu C Aggarwal, and Jiliang Tang. 2019. Graph convolutional networks with eigenpooling. In KDD. 723--731.
[38]
Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology, Vol. 27, 1 (2001).
[39]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546 (2013).
[40]
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In KDD . 701--710.
[41]
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In WSDM . 459--467.
[42]
Ryan A Rossi, Nesreen K Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, and Eunyee Koh. 2019. Heterogeneous network motifs. arXiv preprint arXiv:1901.10026 (2019).
[43]
Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah. 2021 a. Graph Neural Networks for Friend Ranking in Large-scale Social Platforms. In TheWebConf .
[44]
Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah. 2021 b. Graph Neural Networks for Friend Ranking in Large-scale Social Platforms. In Proceedings of the Web Conference 2021. 2535--2546.
[45]
Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann, Disha Makhija, Mohit Kumar, and Christos Faloutsos. 2016. Edgecentric: Anomaly detection in edge-attributed networks. In ICDMW. 327--334.
[46]
Guolei Sun and Xiangliang Zhang. 2019. A novel framework for node/edge attributed graph embedding. In PAKDD. Springer, 169--182.
[47]
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In WWW. 1067--1077.
[48]
Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, and Suhang Wang. 2020 a. Transferring robustness for graph neural network against poisoning attacks. In Proceedings of the 13th International Conference on Web Search and Data Mining . 600--608.
[49]
Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra, and Suhang Wang. 2020 b. Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps. In KDD. 2269--2279.
[50]
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang. 2020 c. Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1435--1444.
[51]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762 (2017).
[52]
Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In ICLR .
[53]
Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In KDD. 1225--1234.
[54]
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018).
[55]
Carl Yang, Xiaolin Shi, Luo Jie, and Jiawei Han. 2018. I know you'll be back: Interpretable new user clustering and churn prediction on a mobile social application. In KDD. 914--922.
[56]
Lin Yao, Luning Wang, Lv Pan, and Kai Yao. 2016. Link prediction based on common-neighbors for dynamic social network. PCS, Vol. 83 (2016).
[57]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018a. Graph convolutional neural networks for web-scale recommender systems. In KDD . 974--983.
[58]
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec. 2018b. Hierarchical graph representation learning with differentiable pooling. arXiv preprint arXiv:1806.08804 (2018).
[59]
Ke Zhang and Konstantinos Pelechrinis. 2014. Understanding spatial homophily: the case of peer influence and social selection. In WWW . 271--282.
[60]
Muhan Zhang and Yixin Chen. 2017. Weisfeiler-lehman neural machine for link prediction. In KDD. 575--583.
[61]
Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. arXiv preprint arXiv:1802.09691 (2018).
[62]
Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, and Suhang Wang. 2020. Semi-Supervised Graph-to-Graph Translation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 1863--1872.

Cited By

View all
  • (2024)Joint inter-word and inter-sentence multi-relation modeling for summary-based recommender systemInformation Processing & Management10.1016/j.ipm.2023.10363161:3(103631)Online publication date: May-2024
  • (2023)Linkless link prediction via relational distillationProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618890(12012-12033)Online publication date: 23-Jul-2023
  • (2023)CARL-G: Clustering-Accelerated Representation Learning on GraphsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599268(2036-2048)Online publication date: 6-Aug-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
February 2022
1690 pages
ISBN:9781450391320
DOI:10.1145/3488560
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 February 2022

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. graph neural networks
  2. social networks
  3. user modeling

Qualifiers

  • Research-article

Funding Sources

Conference

WSDM '22

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)94
  • Downloads (Last 6 weeks)9
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Joint inter-word and inter-sentence multi-relation modeling for summary-based recommender systemInformation Processing & Management10.1016/j.ipm.2023.10363161:3(103631)Online publication date: May-2024
  • (2023)Linkless link prediction via relational distillationProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618890(12012-12033)Online publication date: 23-Jul-2023
  • (2023)CARL-G: Clustering-Accelerated Representation Learning on GraphsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599268(2036-2048)Online publication date: 6-Aug-2023
  • (2023)Embedding Based Retrieval in Friend RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591848(3330-3334)Online publication date: 19-Jul-2023
  • (2022)GStarXProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601710(19810-19823)Online publication date: 28-Nov-2022

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media