TRGCN: A Prediction Model for Information Diffusion Based on Transformer and Relational Graph Convolutional Network
Article No.: 143, Pages 1 - 17
Abstract
In order to capture and integrate the structural features and temporal features contained in social graph and diffusion cascade more effectively, an information diffusion prediction model based on the Transformer and Relational Graph Convolutional Network (TRGCN) is proposed. Firstly, a dynamic heterogeneous graph composed of the social network graph and the diffusion cascade graph was constructed, and it was input into the Relational Graph Convolutional Network (RGCN) to extract the structural features of each node. Secondly, the time embedding of each node was reencoded using Bi-directional Long Short-Term Memory (Bi-LSTM). The time decay function was introduced to give different weights to nodes at different time positions, so as to obtain the temporal features of nodes. Finally, structural features and temporal features were input into Transformer and then merged. The spatiotemporal features are obtained for information diffusion prediction. The experimental results on three real datasets of X (formerly known as Twitter), Douban, and Memetracker show that compared with the optimal model in the comparison experiment, the TRGCN model has an average increase of 4.16% in Hits@100 metric and 13.26% in map@100 metric. The validity and rationality of the model are proved.
References
[1]
Yang Yang, Jie Tang, Cane Leung, Yizhou Sun, Qicong Chen, Juanzi Li, and Qiang Yang. 2015. Rain: Social role-aware information diffusion. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI ’15). 367−373.
[2]
Yunbing Wu, Hang Gao, Weisen Zeng, and Aiying Yin. 2023. Information dissemination prediction by integrating user relationship representation and information dissemination topological features. Computer Science and Exploration 42, 6 (2023), 1–13.
[3]
Chaoqi Yang, Qitian Wu, and Xiaofeng Gao. 2018. EPOC: A survival perspective early pattern detection model for outbreak cascades. In Proceedings of the 2018 International Conference on Database and Expert Systems Applications (ICDESA ’18). 336–351.
[4]
Jin Zhang, Guixiang Zhu, Yuchen Wang, Shuojia Zheng, and Jinglu Chen. 2022. The recommender system of cross-border e-commerce based on heterogeneous graph neural network. Journal of Electronics and Information Technology 44, 11 (2022), 4008–4017.
[5]
Pengfei Zhang, Hongyan Ran, Caiyan Jia, Xuanya Li, and Xueming Han. 2021. A lightweight propagation path aggregating network with neural topic model for rumor detection. Neurocomputing 458, 1 (2021), 468–477.
[6]
Xiaofeng Gao, Zhenhao Cao, Sha Li, Bin Yao, Guihai Chen, and Shaojie Tang. 2019. Taxonomy and evaluation for microblog popularity prediction. ACM Transactions on Knowledge Discovery from Data 13, 2 (2019), 15–40.
[7]
Minjie Ren, Guoqing Jin, Xiaowen Wang, Ruidong Chen, Yunxin Yuan, Weizhi Nie, and Anan Liu. 2022. Microblog popularity prediction algorithm based on XGBoost. Journal of Data Acquisition and Processing 37, 2 (2022), 383–395.
[8]
Hui Ceng, Xiuxiu Gan, Jun Peng, and Weimin Yuan. 2020. Microblog propagation behavior prediction algorithm with multiple features based on double branch structure. Science Technology and Engineering 20, 26 (2020), 10822–10828.
[9]
Huawei Shen, Dashun Wang, Chaoming Song, and Albert-László Barabási. 2014. Modeling and predicting popularity dynamics via reinforced Poisson processes. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI ’14). 291–297.
[10]
Peng Bao, Huawei Shen, Xiaolong Jin, and Xueqi Chen. 2015. Modeling and predicting popularity dynamics of microblogs using self-excited Hawkes processes. In Proceedings of the 24th International Conference on World Wide Web (WWW ’15). 9–10.
[11]
Swapnil Mishra, Marian-Andrei Rizoiu, and Lexing Xie. 2016. Feature driven and point process approaches for popularity prediction. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM ’16). 1069–1078.
[12]
Yue Xu and Guangjie Liu. 2022. Bi-GRU content popularity prediction algorithm based on attention mechanism. Electronic Measurement Technology 45, 3 (2022), 54–60.
[13]
Chunyuan Yuan, Jiacheng Li, Wei Zhou, Yijun Lu, Xiaodan Zhang, and Songlin Hu. 2020. DyHGCN: A dynamic heterogeneous graph convolutional network to learn users’ dynamic preferences for information diffusion prediction. In Proceedings of the Machine Learning and Knowledge Discovery in Databases: European Conference (ECML-PKDD ’20). 347–363.
[14]
Shaobin Liang, Zhihao Chen, Jingjing Wei, Yunbing Wu, and Xiaowen Liao. 2021. Information diffusion prediction based on cascade spatial-temporal feature. Pattern Recognition and Artificial Intelligence 34, 11 (2021), 969–978.
[15]
Yue Xu and Guangjie Liu. 2022. Bi-GRU content popularity prediction algorithm based on attention mechanism. Electronic Measurement Technology 45, 3 (2022), 54–60.
[16]
Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, and Zhiyuan Liu. 2019. Multi-scale information diffusion prediction with reinforced recurrent networks. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI ’19). 4033–4039.
[17]
Jia Wang, Vincent W. Zheng, Zemin Liu, and Kevin Chen-Chuan Chang. 2017. Topological recurrent neural network for diffusion prediction. In Proceedings of the IEEE International Conference on Data Mining (ICDM ’17). 475–484.
[18]
Mohammad Raihanul Islam, Sathappan Muthiah, Bijaya Adhikari, B.Aditya Prakash, and Naren Ramakrishnan. 2018. DeepDiffuse: Predicting the ‘who’ and ‘when’ in cascades. In Proceedings of the IEEE International Conference on Data Mining (ICDM ’18). 1055–1060.
[19]
Cheng Yang, Mmaosong Sun, Haoran Liu, Shiyi Han, Zhiyuan Liu, and Huanbo Luan. 2018. Neural diffusion model for microscopic cascade prediction. IEEE Transactions on Knowledge and Data Engineering 14, 8 (2018), 1–12.
[20]
Zhitao Wang, Chengyao Chen, and Wenjie Li. 2018. A sequential neural information diffusion model with structure attention. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18). 1795–1798.
[21]
Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, and Zhiyuan Liu. 2019. Multi-scale information diffusion prediction with reinforced recurrent networks. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI ’19). 4033–4039.
[22]
Chunyuan Yuan, Jiacheng Li, Wei Zhou, Yijun Lu, Xiaodao Zhang, and Songlin Hu. 2020. DyHGCN: A dynamic heterogeneous graph convolutional network to learn users’ dynamic preferences for information diffusion prediction. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD ’20). 347–363.
[23]
Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR ’17). 1–14.
[24]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Pomero, Pietro Liò, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 2018 International Conference on Machine Learning (ICML ’18). 1–12.
[25]
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 2017 Conference on Neural Information Processing Systems (NIPS ’17). 1025–1035.
[26]
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Lvan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In Proceedings of the 15th International Semantic Web Conference (ISWC ’18). 593–607.
[27]
Jiaxin Sun, Fei Hui, Kaiwang Zhang, Yao Feng, and Shiyuan Zhang. 2023. Traffic volume prediction based on CNN-BiLSTM-AM model. Computer Technology and Development 33, 2 (2023), 32–37+43.
[28]
Shaobin Liang, Zhihao Chen, Jingjing Wei, Yunbing Wu, and Xiaowen Liao. 2021. Information diffusion prediction based on cascade spatial-temporal feature. Pattern Recognition and Artificial Intelligence 34, 11 (2021), 969–978.
[29]
Wei Fan and Yong Liu. 2022. Social network information diffusion prediction based on spatial-temporal transformer. Journal of Computer Research and Development 59, 8 (2022), 1757–1769.
Index Terms
- TRGCN: A Prediction Model for Information Diffusion Based on Transformer and Relational Graph Convolutional Network
Recommendations
Information Diffusion Prediction with Graph Neural Ordinary Differential Equation Network
MM '24: Proceedings of the 32nd ACM International Conference on MultimediaInformation diffusion prediction aims to forecast the path of information spreading in social networks by exploiting user correlations or preferences. Recent works focus on characterizing the dynamic of user preferences and propose to capture users' ...
DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users’ Dynamic Preferences for Information Diffusion Prediction
Machine Learning and Knowledge Discovery in DatabasesAbstractInformation diffusion prediction is a fundamental task for understanding the information propagation process. It has wide applications in such as misinformation spreading prediction and malicious account detection. Previous works either ...
A Dynamic Heterogeneous Graph Perception Network with Time-Based Mini-Batch for Information Diffusion Prediction
Database Systems for Advanced ApplicationsAbstractInformation diffusion prediction is an important task to understand how information spreads among users. Most previous studies either only focused on the use of diffusion sequences, or only used social networks between users to make prediction, ...
Comments
Information & Contributors
Information
Published In
October 2024
189 pages
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].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 October 2024
Online AM: 26 July 2024
Accepted: 21 May 2024
Revised: 23 April 2024
Received: 15 June 2023
Published in TALLIP Volume 23, Issue 10
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Natural Science Foundation of China
- Education and Scientific Research Project of Shanghai
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 229Total Downloads
- Downloads (Last 12 months)229
- Downloads (Last 6 weeks)23
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in