Abstract
Bipartite networks are capable of representing complex systems that involve two distinct types of objects. However, there are limitations to the existing bipartite networks: 1) It is inadequate in characterizing multi-relationships among objects in complex systems, as it is restricted to depict only one type of relationship. 2) It is limited to static representations of complex systems, hampering their ability to describe dynamic changes in the interactions among objects over time. Therefore, the Dynamic Multi-Relationship Bipartite Network (DMBN) model is introduced, which not only models the dynamic multi-relationships between two types of objects in complex systems, but also enables dynamic prediction of the intricate relationships between objects. Extensive experiments were conducted on complex systems, and the results indicate that the DMBN model is significantly better than the baseline methods across multiple evaluation metrics, thereby proving the effectiveness of the DMBN.
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Lv, H., Zou, G., Zhang, B. (2024). Construction and Prediction of a Dynamic Multi-relationship Bipartite Network. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_25
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DOI: https://doi.org/10.1007/978-981-99-8145-8_25
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