CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909318662586368 |
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| author | Zhu, Hongjun Yuan, Shun Liu, Xin Chen, Kuo Jia, Chaolong Qian, Ying |
| author_facet | Zhu, Hongjun Yuan, Shun Liu, Xin Chen, Kuo Jia, Chaolong Qian, Ying |
| contents | Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by its superior learning and representation capabilities, mitigates the shortcomings inherent of the other methods. However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focus on the sequential order of user activations often neglect the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades.To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, the CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multi-task learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_04961 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks Zhu, Hongjun Yuan, Shun Liu, Xin Chen, Kuo Jia, Chaolong Qian, Ying Social and Information Networks Machine Learning Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by its superior learning and representation capabilities, mitigates the shortcomings inherent of the other methods. However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focus on the sequential order of user activations often neglect the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades.To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, the CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multi-task learning. |
| title | CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2308.04961 |