Summary of HNR2020 Keynote Petter Holme "Temporal Networks"

00:00:00 - 00:30:00

In the video, Petter Holme discusses temporal networks and their importance in understanding complex systems. He provides an overview of the concepts and gives examples of how temporal networks can be used to study the spread of disease and other phenomena.

• 00:00:00 Pepper Alma discusses the field of temporal networks, which is broadly related to a variety of scientific disciplines, including physics, social sciences, and humanities. She discusses how networks are ubiquitous and can be used to understand complex systems. Lastly, she provides an example of a network from historical research.
• 00:05:00 Petter Holme presents a keynote on temporal networks, which are networks of nodes that can be structured in the network and time of the interaction, influencing what we are interested in. Social interaction has been studied in many different ways, and temporal networks are a way to study these networks more systematically.
• 00:10:00 Petter Holme, a keynote speaker at the HNR2020 conference, discusses the importance of temporal networks in understanding how diseases spread. He explains that there are two ways to view how diseases spread: with compartmental models, which view the spread of a disease as a fixed process, and with temporal networks, which consider the influence of distant nodes. Holme concludes the talk by discussing how temporal networks can be used to model the spread of ideas and other phenomena.
• 00:15:00 The speaker defines temporal networks and argues that they can be a good underlying structure for modeling disease spreading. He goes on to discuss a study that used temporal networks to study online prostitution.
• 00:20:00 The main takeaway from this talk is that temporal networks are similar to static networks, but have time-dependence. Various concepts from static network theory can be applied to temporal networks, but must be done in a time-dependent way. Additionally, important actors can be identified by measuring their importance and determining if they are part of a community.
• 00:25:00 The researchers studied how temporal networks can be used to identify important nodes in a population and to reduce the spread of a disease. They found that temporal networks can be used to find important nodes and to reduce the spread of a disease, depending on the data set.
• 00:30:00 This video discusses temporal networks, which are networks that have time as an important aspect. The video provides a brief overview of temporal networks and their concepts, as well as some examples of temporal motifs.