Network Model Selection Using Task-Focused Minimum Description Length
This addresses the challenge of implicit choices in network definition for users across various application domains, though it appears incremental as it builds on existing MDL and efficiency concepts.
The paper tackles the problem of selecting network models from data by proposing a task-focused methodology using minimum description length (MDL) criteria to measure efficiency for node-level predictive tasks, favoring parsimonious models to avoid overfitting.
Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represented. Users of downstream network data may not even be aware of these choices or their impacts. We propose a task-focused network model selection methodology which addresses several key challenges. Our approach constructs network models from underlying data and uses minimum description length (MDL) criteria for selection. Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i.e. node-level) predictive task of interest. Selection on efficiency favors parsimonious (e.g. sparse) models to avoid overfitting and can be applied across arbitrary tasks and representations. We show stability, sensitivity, and significance testing in our methodology.