Before revision:  To address this challenge, we introduce VulnDS: a top-k vulnerable SME detection system for large-scale financial networks, which is deployed in our collaborated bank. First, we model the risks of the guaranteed-loan network by a probabilistic graph, which consists of the guarantee-loan network structure, self-risk probability for the nodes and diffusion probability for the edges. Moreover, to identify the vulnerable enterprises, we propose a sampling-based approach with tight theoretical guarantee .
After revision:  To address this challenge, we introduce VulnDS: a top-k vulnerable nodes detection in uncertain graphs. We formally define the problem and prove its hardness. To identify the k most vulnerable nodes, a sampling-based approach with tight theoretical guarantee .