The analysis of patent data is important in various domains such as R&D planning, strategic management and investment, litigation or human resources management. Patent data can be modeled as a network with different kinds of nodes (patents, inventors, applicants, …) and different kinds of edges (citation, co-authorship, …). Here we focus on inventors and their co-authorship network. The temporal aspects of patent data allows for the exploration of the inventors' dynamic behavior and of their changing performance measures over time.
The candidate, according to the assigned scope (SE/PR/Bak/DA, see table below), has to perform the following activities:
State-of-The-Art Report: write an annotated survey of existing methods and tools for the analysis of dynamic graphs/networks.
Basic features: prototypically implement existing visual analytics methods to be applied to inventor networks.
Advanced features: develop new specific visual analytics method to interactively explore the dynamics of patent networks on different scales of temporal granularity.
Scientific summary: summarize the scientific contribution of the work, with reflections on the lesson learned and future advancement in this research field.
|Writing State-of-The-Art Report||√||√||√|
|Coding basic features||√||√||√|
|Designing/coding adv. features||√|
|Writing scientific summary||√||√|