Equivariant Diffusion-Based Sequential Hypergraph Neural Networks with Co-attention Fusion for Information Diffusion Prediction
Paper
Lu, Ye, Zhang, Ji, Yu, Ting and Yang, Gaoming. 2025. "Equivariant Diffusion-Based Sequential Hypergraph Neural Networks with Co-attention Fusion for Information Diffusion Prediction." 25th International Conferenc on Web Information Systems Engineering (WISE 2024). Doha, Qatar 02 - 05 Dec 2024 Singapore . Springer. https://doi.org/10.1007/978-981-96-0573-6_6
| Paper/Presentation Title | Equivariant Diffusion-Based Sequential Hypergraph Neural Networks with Co-attention Fusion for Information Diffusion Prediction |
|---|---|
| Presentation Type | Paper |
| Authors | Lu, Ye, Zhang, Ji, Yu, Ting and Yang, Gaoming |
| Journal or Proceedings Title | Proceedings of the 25th International Conferenc on Web Information Systems Engineering (WISE 2024) |
| Journal Citation | 15439, pp. 76-89 |
| Number of Pages | 14 |
| Year | 2025 |
| Publisher | Springer |
| Place of Publication | Singapore |
| ISSN | 0302-9743 |
| 1611-3349 | |
| ISBN | 9789819605699 |
| 9789819605705 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-96-0573-6_6 |
| Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-96-0573-6_6 |
| Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-96-0573-6 |
| Conference/Event | 25th International Conferenc on Web Information Systems Engineering (WISE 2024) |
| Event Details | 25th International Conferenc on Web Information Systems Engineering (WISE 2024) Delivery In person Event Date 02 to end of 05 Dec 2024 Event Location Doha, Qatar |
| Abstract | Information spread within social networks is a complex process with broad implications. Predicting information diffusion is crucial for understanding information spread within social networks. However, previous research has primarily focused on the homogeneity characteristics of internal cascades, such as temporal and social relationships, neglecting the impact of external information propagation. Additionally, conventional methods of feature integration simply merge cascade and user embeddings, which may introduce excessive redundant information and result in the loss of valuable contextual information critical for accurate predictions. To address these limitations, we present a novel model, the Equivariant Diffusion-based Sequential Hypergraph Neural Network with Co-Attention Fusion (EDSHNN-CAF). Within its cascade feature learning module, the model proposes hypergraphs with equivariant diffusion operators to incorporate external cascade influences alongside internal features. This approach effectively captures complex high-order interconnections and accurately reflects the dynamics of information diffusion. In the feature fusion and prediction module, a co-attention mechanism is designed to seamlessly integrate cascade and user embeddings, revealing their complex interdependencies and significantly enhancing predictive capabilities. Experimental results on four real datasets showcase the promising performance of EDSHNN-CAF in predicting information diffusion, outperforming existing state-of-the-art information diffusion prediction models. |
| Keywords | Hypergraph neural networks; Information diffusion prediction; Social networks |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Series | Lecture Notes in Computer Science |
| Byline Affiliations | Nanjing University of Aeronautics and Astronautics, China |
| University of Southern Queensland | |
| Zhejiang Lab, China | |
| Anhui University of Science and Technology, China |
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https://research.usq.edu.au/item/zx20q/equivariant-diffusion-based-sequential-hypergraph-neural-networks-with-co-attention-fusion-for-information-diffusion-prediction
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