Link prediction on multi-relational graphs from an influence propagation perspective
Predicting the existence and type of links (edges) between nodes in a multi-relational graph is key for applications from social interaction prediction to knowledge relationship identification. Enhancing local features with relevant global information is crucial for accurate link prediction, yet it remains challenging. We address this by modeling the relationship between node pairs as node influence. That is, whether the node influence can be propagated and what type of influence is propagated indicates where and what type the edge is, which will be the most relevant local and global information to predict the edges. To this end, we extend the Susceptible-Infectious-Recovered (SIR) epidem...
| Authors | Zidu Yin, Yuankai Qi, Dong Gong et al. |
|---|---|
| Primary category | cs.SI |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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