research-article

Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior

Authors:

Prasha Shrestha,

Suraj Maharjan,

Svitlana VolkovaAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2033 - 2042

https://doi.org/10.1145/3357384.3358043

Published: 03 November 2019 Publication History

Abstract

Most of the existing graph analytics for understanding social behavior focuses on learning from static rather than dynamic graphs using hand-crafted network features or recently emerged graph embeddings learned independently from a downstream predictive task, and solving predictive (e.g., link prediction) rather than forecasting tasks directly. To address these limitations, we propose (1) a novel task -- forecasting user interactions over dynamic social graphs, and (2) a novel deep learning, multi-task, node-aware attention model that focuses on forecasting social interactions, going beyond recently emerged approaches for learning dynamic graph embeddings. Our model relies on graph convolutions and recurrent layers to forecast future social behavior and interaction patterns in dynamic social graphs. We evaluate our model on the ability to forecast the number of retweets and mentions of a specific news source on Twitter (focusing on deceptive and credible news sources) with R^2 of 0.79 for retweets and 0.81 for mentions. An additional evaluation includes model forecasts of user-repository interactions on GitHub and comments to a specific video on YouTube with a mean absolute error close to 2% and R^2 exceeding 0.69. Our results demonstrate that learning from connectivity information over time in combination with node embeddings yields better forecasting results than when we incorporate the state-of-the-art graph embeddings e.g., Node2Vec and DeepWalk into our model. Finally, we perform in-depth analyses to examine factors that influence model performance across tasks and different graph types e.g., the influence of training and forecasting windows as well as graph topological properties.

References

[1]

Peter J Brockwell, Richard A Davis, and Matthew V Calder. 2002. Introduction to time series and forecasting. Vol. 2. Springer.

[2]

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann Lecun. 2014. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations (ICLR2014), CBLS, April 2014 .

[3]

Dorota Celi'nska. 2018. Coding Together in a Social Network: Collaboration Among GitHub Users. In Proceedings of the 9th International Conference on Social Media and Society (SMSociety '18). ACM, New York, NY, USA, 31--40. https://doi.org/10.1145/3217804.3217895

Digital Library

[4]

Hsinchun Chen, Xin Li, and Zan Huang. 2005. Link prediction approach to collaborative filtering. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, 2005(JCDL). IEEE, 141--142.

[5]

Ronan Collobert and Jason Weston. 2008. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. In Proceedings of the 25th International Conference on Machine Learning (ICML). 160--167.

Digital Library

[6]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3844--3852.

[7]

Li Deng, Dong Yu, et almbox. 2014. Deep learning: methods and applications. Foundations and Trends® in Signal Processing, Vol. 7, 3--4 (2014), 197--387.

[8]

Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. arXiv:1704.01212 (2017).

[9]

Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research), Yee Whye Teh and Mike Titterington (Eds.), Vol. 9. 249--256.

[10]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning .MIT Press. http://www.deeplearningbook.org.

Digital Library

[11]

Georgios Gousios and Diomidis Spinellis. 2012. GHTorrent: GitHub's data from a firehose. In Proceedings of Mining Software Repositories . 12--21.

[12]

Aditya Grover and Jure Leskovec. 2016. Node2Vec: Scalable Feature Learning for Networks. In Proceedings of ACM SIGKDD . 855--864.

Digital Library

[13]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017a. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1025--1035.

[14]

William L Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017).

[15]

Isaac Henrion, Johann Brehmer, Joan Bruna, Kyunghun Cho, Kyle Cranmer, Gilles Louppe, and Gaspar Rochette. 2017. Neural Message Passing for Jet Physics. (2017).

[16]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. CoRR, Vol. abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980

[17]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR) .

[18]

Gü nter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. 2017. Self-Normalizing Neural Networks. CoRR, Vol. abs/1706.02515 (2017). http://arxiv.org/abs/1706.02515

[19]

Yann LeCun, Léon Bottou, Genevieve B. Orr, and Klaus-Robert Müller. 1998. Efficient BackProp. In Neural Networks: Tricks of the Trade, This Book is an Outgrowth of a 1996 NIPS Workshop. 9--50.

[20]

Antonio Lima, Luca Rossi, and Mirco Musolesi. 2014. Coding together at scale: GitHub as a collaborative social network. In Eighth International AAAI Conference on Weblogs and Social Media .

[21]

Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, and Sungchul Kim. 2018. Continuous-time dynamic network embeddings. In 3rd International Workshop on Learning Representations for Big Networks (WWW BigNet) .

Digital Library

[22]

Ping-Feng Pai and Chih-Sheng Lin. 2005. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, Vol. 33, 6 (2005), 497--505.

[23]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. In Proceedings of ACM SIGKDD . 701--710.

Digital Library

[24]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale Information Network Embedding. In Proceedings of the 24th International Conference on World Wide Web (WWW '15). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1067--1077. https://doi.org/10.1145/2736277.2741093

Digital Library

[25]

Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. 3462--3471.

[26]

Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2017. Graph Attention Networks. arXiv preprint arXiv:1710.10903 (2017).

[27]

Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, and Nathan Hodas. 2017. Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . 647--653.

[28]

Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations (ICLR) .

[29]

Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model. In Proceedings of the 35th International Conference on Machine Learning (ICML). 5694--5703.

[30]

Wenchao Yu, Charu C Aggarwal, and Wei Wang. 2017. Temporally factorized network modeling for evolutionary network analysis. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 455--464.

Digital Library

[31]

Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2018. SINE: Scalable Incomplete Network Embedding. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 737--746.

[32]

G Peter Zhang. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, Vol. 50 (2003), 159--175.

[33]

Le-kui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018. Dynamic Network Embedding by Modeling Triadic Closure Process. In AAAI .

[34]

Tao Zhou, Jie Ren, Matúvs Medo, and Yi-Cheng Zhang. 2007. Bipartite network projection and personal recommendation. Physical Review E, Vol. 76, 4 (2007), 046115.

Cited By

Iamnitchi AHall LHorawalavithana SMubang FNg KSkvoretz J(2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
https://doi.org/10.3389/fdata.2023.1135191
Ng KMubang FHall LSkvoretz JIamnitchi A(2023)Experimental evaluation of baselines for forecasting social media timeseriesEPJ Data Science10.1140/epjds/s13688-023-00383-912:1Online publication date: 27-Mar-2023
https://doi.org/10.1140/epjds/s13688-023-00383-9
Mubang FHall L(2023)VAM: An End-to-End Simulator for Time Series Regression and Temporal Link Prediction in Social Media NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318058610:4(1479-1490)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2022.3180586
Show More Cited By

Index Terms

Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior

Recommendations

A Survey on Embedding Dynamic Graphs
Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic ...
Dynamic graphs attention for ocean variable forecasting
Abstract
Forecasting the ocean dynamics is a critical issue for a wide array of climate extremes and environmental crisis. The dynamic variations are traditionally approached by relying on numerical models with all the related physical processes ...
On cop-win dynamic graphs
Abstract
We extend the Nowakowski–Winkler characterisation of cop-win graphs to dynamic graphs and we extend the Hahn–MacGillivray algorithm for recognising k-cop-win graphs to dynamic graphs.

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 ACM.

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 ACM 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: 03 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '19

Sponsor:

CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

Acceptance Rates

CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
412
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)2

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Iamnitchi AHall LHorawalavithana SMubang FNg KSkvoretz J(2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
https://doi.org/10.3389/fdata.2023.1135191
Ng KMubang FHall LSkvoretz JIamnitchi A(2023)Experimental evaluation of baselines for forecasting social media timeseriesEPJ Data Science10.1140/epjds/s13688-023-00383-912:1Online publication date: 27-Mar-2023
https://doi.org/10.1140/epjds/s13688-023-00383-9
Mubang FHall L(2023)VAM: An End-to-End Simulator for Time Series Regression and Temporal Link Prediction in Social Media NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318058610:4(1479-1490)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2022.3180586
Mubang FHall L(2023)Using SMOTE-based Data Augmentation for Social Media Time Series Prediction2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394544(893-898)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394544
Zong CZhuang YShao JLu W(2023)Citation Prediction via Influence Representation Using Temporal GraphsBig Data and Social Computing10.1007/978-981-99-3925-1_14(221-237)Online publication date: 30-Jun-2023
https://doi.org/10.1007/978-981-99-3925-1_14
Glenski MAyton ESoni SSaldanha EArendt DQuiter BCooper RVolkova S(2022)Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public InformationIEEE Transactions on Nuclear Science10.1109/TNS.2022.316221669:6(1375-1384)Online publication date: Jun-2022
https://doi.org/10.1109/TNS.2022.3162216
Mubang FHall L(2022)Simulating New and Old Twitter User Activity with XGBoost and Probabilistic Hybrid Models2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00026(132-139)Online publication date: Dec-2022
https://doi.org/10.1109/ICMLA55696.2022.00026
Ng KHorawalavithana SIamnitchi A(2022)Social media activity forecasting with exogenous and endogenous signalsSocial Network Analysis and Mining10.1007/s13278-022-00927-312:1Online publication date: 8-Aug-2022
https://doi.org/10.1007/s13278-022-00927-3
Barros CMendonça MVieira AZiviani A(2021)A Survey on Embedding Dynamic GraphsACM Computing Surveys10.1145/348359555:1(1-37)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1145/3483595

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten