aiqbench analysis
This research introduces IGNP, a novel framework for link prediction in multi-relational graphs that models node relationships through influence propagation mechanisms inspired by epidemic dynamics. By extending the SIR model and compressing sub-graphs via virtual edges, the method effectively balances global information integration with computational efficiency on large-scale datasets. The approach demonstrates significant performance gains over existing baselines, offering a robust solution for tasks ranging from social interaction forecasting to knowledge graph completion.
Link prediction on multi-relational graphs from an influence propagation perspective
arXiv:2608.05016v1 Announce Type: cross Abstract: 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) epidemic model to capture the influence propagation of nodes on a large scale through sub-graph structures. Subsequently, these sub-graphs are compressed using virtual edges, thereby substantially reducing the computation associated with utilizing the global graph structure. Finally, we propose the Influential Graph Neural Predictor, referred to as IGNP, a link prediction framework guided by influence propagation. Extensive experiments demonstrate the superiority of the proposed method, which outperforms strong baselines by a large margin on the widely used and real-world datasets.
Abstract: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) epidemic model to capture the influence propagation of nodes on a large scale through sub-graph structures. Subsequently, these sub-graphs are compressed using virtual edges, thereby substantially reducing the computation associated with utilizing the global graph structure. Finally, we propose the Influential Graph Neural Predictor, referred to as IGNP, a link prediction framework guided by influence propagation. Extensive experiments demonstrate the superiority of the proposed method, which outperforms strong baselines by a large margin on the widely used and real-world datasets.
Social and Information Networks (cs.SI); Machine Learning (cs.LG)
arXiv:2608.05016 [cs.SI]
(or arXiv:2608.05016v1 [cs.SI] for this version)
https://doi.org/10.48550/arXiv.2608.05016 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1016/j.patcog.2026.114039 Focus to learn more DOI(s) linking to related resources
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