DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks
This work addresses influence maximization for social network analysts by providing a heuristic alternative to preset diffusion models, though it is incremental as it builds on prior statistical approaches.
The paper tackles the data-driven influence maximization problem in social networks by proposing DSCom, a framework that uses node attributes and machine learning to infer relationships and apply spectral clustering, achieving efficiency and effectiveness in empirical experiments on real-world networks.
Influence maximization aims to find a subset of seeds that maximize the influence spread under a given budget. In this paper, we mainly address the data-driven version of this problem, where the diffusion model is not given but needs to be inferred from the history cascades. Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee. However, in their settings, though the diffusion parameters are inferred, they still need users to preset the diffusion model, which can be an intractable problem in real-world practices. In this paper, we reformulate the problem on the attributed network and leverage the node attributes to estimate the closeness between the connected nodes. Specifically, we propose a machine learning-based framework, named DSCom, to address this problem in a heuristic way. Under this framework, we first infer the users' relationship from the diffusion dataset through attention mechanism and then leverage spectral clustering to overcome the influence overlap problem in the lack of exact diffusion formula. Compared to the previous theoretical works, we carefully designed empirical experiments with parameterized diffusion models based on real-world social networks, which prove the efficiency and effectiveness of our algorithm.