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Attention Network for Information Diffusion Prediction

Authors:

Zhitao Wang,

Chengyao Chen,

Wenjie LiAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 65 - 66

https://doi.org/10.1145/3184558.3186931

Published: 23 April 2018 Publication History

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Abstract

In this paper, we propose an attention network for diffusion prediction problem. The developed diffusion attention module can effectively explore the implicit user-to-user diffusion dependency among information cascade users. Besides, the user-to-cascade importance and the time-decay effect are captured and utilized by the model. The superiority of the proposed model over state-of-the-art methods is demonstrated by experiments on real diffusion data.

References

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Cited By

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Liu XMiao CFiumara GDe Meo P(2024)Information Propagation Prediction Based on Spatial–Temporal Attention and Heterogeneous Graph Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324457311:1(945-958)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3244573
Yang CWang HTang JShi CSun MCui GLiu Z(2023)Full-Scale Information Diffusion Prediction With Reinforced Recurrent NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.310615634:5(2271-2283)Online publication date: May-2023
https://doi.org/10.1109/TNNLS.2021.3106156
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

Attention Network for Information Diffusion Prediction
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

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Published In

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

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]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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WWW '18

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IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Liu XMiao CFiumara GDe Meo P(2024)Information Propagation Prediction Based on Spatial–Temporal Attention and Heterogeneous Graph Convolutional NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324457311:1(945-958)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3244573
Yang CWang HTang JShi CSun MCui GLiu Z(2023)Full-Scale Information Diffusion Prediction With Reinforced Recurrent NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.310615634:5(2271-2283)Online publication date: May-2023
https://doi.org/10.1109/TNNLS.2021.3106156
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
Chen JXu XChen LRuan ZMing ZLiu Y(2023)CTL-DIFF: Control Information Diffusion in Social Network by Structure OptimizationIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.316573910:3(1115-1129)Online publication date: Jun-2023
https://doi.org/10.1109/TCSS.2022.3165739
Shao YHe HTai YWu XYang H(2023)A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion PredictionPattern Recognition and Computer Vision10.1007/978-981-99-8546-3_15(182-194)Online publication date: 13-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-99-8546-3_15
Xu SZhou LXu JWang LChen H(2022)MSIDP: Multi-scale Information Diffusion Prediction with Timestamp Information and Wide Dispersion2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892786(1-10)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892786
Pan LXiong YLi BHuang TWan W(2022)Feature attenuation reinforced recurrent neural network for diffusion predictionApplied Intelligence10.1007/s10489-022-03413-753:2(1855-1869)Online publication date: 3-May-2022
https://dl.acm.org/doi/10.1007/s10489-022-03413-7
Wang DWei LYuan CBao YZhou WZhu XHu S(2022)Cascade-Enhanced Graph Convolutional Network for Information Diffusion PredictionDatabase Systems for Advanced Applications10.1007/978-3-031-00123-9_50(615-631)Online publication date: 11-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-00123-9_50
Huang NZhou GZhang MZhang MYu Z(2021)Modelling the Latent Semantics of Diffusion Sources in Information Cascade PredictionComputational Intelligence and Neuroscience10.1155/2021/78802152021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/7880215
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
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