A probabilistic generative model for latent business networks mining

The structural embeddedness theory posits that a company's embeddedness in a business network impacts its competitive performance. This highlights the theoretical and practical values toward business network mining and analysis. Given the fact that latent business relationships exist among companies, and these relationships continuously evolve over time, a manual approach for the discovery and analysis of business network is ineffective. Though numerous research has been devoted to social network discovery and analysis, relatively little research work is performed for business network discovery. Guided by the design science research methodology, the main contribution of our research is the design and development of a novel probabilistic generative model for latent business relationship mining. The proposed method can effectively and efficiently discover evolving latent business networks over time. Our experimental results confirm that the proposed method outperforms the vector space model based baseline method by 28% in terms of average AUC value.