Inferring information diffusion networks plays a crucial role in social network analysis and various applications. Existing methods often rely on the infection times of nodes in diffusion processes to uncover influence relationships. However, accurately monitoring real-time temporal information is challenging and resource-intensive. Additionally, some approaches that do not utilize infection timestamps fail to adequately capture the strength of influence relationships among nodes.

To address these limitations, we propose a novel method called Learning Influence Probabilities in diffusion Networks without timestamps (LIPN). LIPN introduces an enhanced correlation metric to measure the relationship between node infections, which is utilized in the pre-pruning stage to mitigate the negative impact of redundant candidate edges during the inference process. LIPN constructs a likelihood function for the diffusion process by considering the infection probability between nodes.

Furthermore, to enhance the reliability of the inferred results, LIPN incorporates an optimization strategy that combines an expectation maximization algorithm with a variant of the simulated annealing algorithm.

The experimental results validate the effectiveness of LIPN in both synthetic networks and real-world networks, highlighting its potential for empowering social network analysis and applications.

Y. Wang, H. Wang, C. Gao, K. Fan, H. Cheng, Z. Shen, Z. Wang, M. Perc, Learning influence probabilities in diffusion networks without timestamps, Applied Mathematics and Computation 503 (2025) 129502.