The Xerberus Risk Model is addresses the [[Price Risk]] within the [[Crypto Risk Problem]]. The output of the Xerberus Risk Model are fair [[Risk Rating]] that make Crypto Assets truly comparable. The Xerberus Risk Model is based on the assumption that the value of [[Crypto Assets]] comes from there network. This is foundational assumption and the validity of the Xerberus Risk Ratings stands or falls with this [[Network Assumption]]. We measure the network of a crypto Asset using the [[Wallet Graph]]. ### The Xerberus Risk Model: A Hybrid Approach of Machine Learning and Topology The Xerberus Risk Model combines a machine learning module with a mathematical model. The mathematical model processes the Wallet Graph, transforming it into [[Topological Shapes]]. These shapes are then analyzed for specific attributes that correlate with positive or negative implications for asset price and the long-term health of the network. ### Understanding the Xerberus Risk Model’s Wallet Graph Analysis Our model ultimately focuses on studying the wallet graph. While it is complex to explain precisely what our model measures, it can be understood through examples of dimensions that our quantitative research has shown to be critical for the health of crypto asset networks. **These key dimensions include:** [[Distribution]] [[Decentralisation]] [[Diversity]] [[Flow]] [[Wallet Cluster]] [[Emission]] [[Manipulation and Crime]] [[Life-Cycle]]