research-article

Information Diffusion Prediction with Network Regularized Role-based User Representation Learning

Authors:

Wenjie LiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 3

Article No.: 29, Pages 1 - 23

https://doi.org/10.1145/3314106

Published: 07 May 2019 Publication History

Abstract

In this article, we aim at developing a user representation learning model to solve the information diffusion prediction problem in social media. The main idea is to project the diffusion users into a continuous latent space as the role-based (sender and receiver) representations, which capture unique diffusion characteristics of users. The model learns the role-based representations based on a cascade modeling objective that aims at maximizing the likelihood of observed cascades, and employs the matrix factorization objective of reconstructing structural proximities as a regularization on representations. By jointly embedding the information of cascades and network, the learned representations are robust on different diffusion data. We evaluate the proposed model on three real-world datasets. The experimental results demonstrate the better performance of the proposed model than state-of-the-art diffusion embedding and network embedding models and other popular graph-based methods.

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Cited By

Chen ZSun A(2024)DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World NetworkACM Transactions on Knowledge Discovery from Data10.1145/364946018:6(1-20)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3649460
Zhao YYao XHuang H(2024)A continuous-time diffusion model for inferring multi-layer diffusion networksApplied Intelligence10.1007/s10489-024-05620-w54:17-18(8200-8223)Online publication date: 24-Jun-2024
https://doi.org/10.1007/s10489-024-05620-w
Feng SZhao KFang LFeng KWei WLi XShao L(2023)H-Diffu: Hyperbolic Representations for Information Diffusion PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.320906735:9(8784-8798)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3209067
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Index Terms

Information Diffusion Prediction with Network Regularized Role-based User Representation Learning

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 3

June 2019

261 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3331063

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 May 2019

Accepted: 01 February 2019

Revised: 01 October 2018

Received: 01 April 2018

Published in TKDD Volume 13, Issue 3

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National Natural Science Foundation of China
The Hong Kong Polytechnic University

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Cited By

Chen ZSun A(2024)DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World NetworkACM Transactions on Knowledge Discovery from Data10.1145/364946018:6(1-20)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3649460
Zhao YYao XHuang H(2024)A continuous-time diffusion model for inferring multi-layer diffusion networksApplied Intelligence10.1007/s10489-024-05620-w54:17-18(8200-8223)Online publication date: 24-Jun-2024
https://doi.org/10.1007/s10489-024-05620-w
Feng SZhao KFang LFeng KWei WLi XShao L(2023)H-Diffu: Hyperbolic Representations for Information Diffusion PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.320906735:9(8784-8798)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3209067
Jiao PTian QZhang WGuo XJin DWu H(2023)Role Discovery-Guided Network Embedding Based on Autoencoder and Attention MechanismIEEE Transactions on Cybernetics10.1109/TCYB.2021.309489353:1(365-378)Online publication date: Jan-2023
https://doi.org/10.1109/TCYB.2021.3094893
Mou XXu WZhu YLi QXiao Y(2023)A Social Topic Diffusion Model Based on Rumor, Anti-Rumor, and Motivation-RumorIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.317943510:5(2644-2659)Online publication date: Oct-2023
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Xiao YYuan WYue XLi TLi Q(2023)A Diffusion Model for Multimessage Multidimensional Complex Game Based on Rumor and Anti-RumorIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.317941710:5(2672-2685)Online publication date: Oct-2023
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Xiang TLi QLi WXiao Y(2023)A rumor heat prediction model based on rumor and anti-rumor multiple messages and knowledge representationInformation Processing & Management10.1016/j.ipm.2023.10333760:3(103337)Online publication date: May-2023
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Wang ZChen CLi W(2022)Joint Learning of User Representation With Diffusion Sequence and Network StructureIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299507534:3(1275-1287)Online publication date: 1-Mar-2022
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Yang SZhang ZJiang HZheng CZhao X(2022)Topic-Aware Popularity and Retweeter Prediction Model for Cascade Study2022 IEEE 9th International Conference on Cyber Security and Cloud Computing (CSCloud)/2022 IEEE 8th International Conference on Edge Computing and Scalable Cloud (EdgeCom)10.1109/CSCloud-EdgeCom54986.2022.00038(172-179)Online publication date: Jun-2022
https://doi.org/10.1109/CSCloud-EdgeCom54986.2022.00038
Shang JWang YGong YZou YCai X(2022)A Graph Neural Network-Based Approach for Predicting Second Rise of Information Diffusion on Social NetworksComputer Supported Cooperative Work and Social Computing10.1007/978-981-19-4549-6_27(352-363)Online publication date: 22-Jul-2022
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