[#39] Applying modularity to temporal networks modelled as link streams & Managing PhD and industrial work
Temporal networks are commonly used to model real-life phenomena. When these phenomena represent interactions and are captured at a fine-grained temporal resolution, they are modeled as link streams. Community detection is an essential network analysis task. Although many methods exist for static networks, and some methods have been developed for temporal networks represented as sequences of snapshots, few works can handle directly link streams. We propose the first adaptation of the well-known Modularity quality function to link streams. Unlike existing methods, it is independent of the time scale of analysis.
Challenges include managing time effectively, as balancing the demands of both a PhD and a corporate job often leads to burnout. Conflicting priorities and overlapping deadlines from both sides can cause mental strain. Switching between the deep focus needed for research and the fast pace of corporate tasks is mentally exhausting. Additionally, limited flexibility at work and unrelated job tasks can further hinder progress.
As per tradition, Victor started with his research topic. In his PhD, he’s investigating how to extend the modularity quality function to temporal networks modeled as link streams, demonstrating that this definition is independent of the time scale of analysis.
In the discussion, Victor shared his experience of doing a part-time PhD while working part-time at a company. Among the benefits was the opportunity to explore two different fields and workplaces.
As the discussion went on, it became clear how complex it can be to manage both. This depends on how related the jobs are, how one manages time, and handles pressure. The open question is how transferable the skills learned from each place are.