Inferring Network Structure From Data
This addresses the issue of unexamined choices in translating data to networks for researchers and practitioners in network analysis, but it is incremental as it builds on existing network modeling approaches.
The paper tackles the problem of selecting network models from raw data by proposing a methodology that evaluates network utility for tasks and selects the most parsimonious model, demonstrating that network definition significantly impacts modeling system behavior.
Networks are complex models for underlying data in many application domains. In most instances, raw data is not natively in the form of a network, but derived from sensors, logs, images, or other data. Yet, the impact of the various choices in translating this data to a network have been largely unexamined. In this work, we propose a network model selection methodology that focuses on evaluating a network's utility for varying tasks, together with an efficiency measure which selects the most parsimonious model. We demonstrate that this network definition matters in several ways for modeling the behavior of the underlying system.