A continuous-time diffusion model for inferring multi-layer diffusion networks

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 54; no. 17-18; pp. 8200 - 8223
• Main Authors: Zhao, Yunpeng, Yao, Xiaopeng, Huang, Hejiao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Science; Datasets; Diffusion layers; Diffusion models; Embedding; False information; Heuristic; Heuristic methods; Machines; Manufacturing; Mechanical Engineering; Methods; Modelling; Multilayers; Networks; Optimization; Optimization algorithms; Optimization techniques; Parameters; Processes; Relationship strength; Information cascades; Network inference; Learnable parameters; Embedding-based modeling; Trust strength
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-024-05620-w

Inferring multilayer diffusion networks from observed cascades is both crucial and realistic. To infer multilayer diffusion networks, constructing continuous-time diffusion models that capture diffusion dynamics is a prerequisite. However, developing such models faces two main challenges: (1) reducing the number of learnable parameters for precise optimization with limited cascades while effectively modeling for accurate inference and (2) adapting the models to more realistic scenarios. In this paper, we propose a novel continuous-time diffusion model, namely the Embedding-based Continuous-time Diffusion (ECD) model, which employs an embedding method while modeling symmetric relationship strength, asymmetric relationship strength, and trust strength. Specifically, by leveraging the embedding method, the number of learnable parameters is significantly reduced compared with previous models. Then, by modeling symmetric relationship strength, our model can be used in scenarios where the relationships between nodes are symmetric. Subsequently, the trust strength can be inferred by our proposed efficient heuristic algorithm, making our model suitable for scenarios where time information is unavailable. Furthermore, we develop an optimization algorithm to optimize the proposed model and infer multilayer diffusion networks. The experimental results on synthetic and real datasets show that our model and algorithms outperform the comparison methods.