Great touching on temporality. A network is a snapshot frozen in time. If you think of Time as an emergent physical property, not as a ticking clock, you can see a network evolves say through wiring and rewiring of nodes as they age and die at the experimental or theoretical rate you give them. Here differential and difference equations become handy for describing time equations for each node.
Thank you so much Martin Grandjean for this great introductory series. The graphics were beautiful and really helped to emphasize and understand the points you made. I think it's really important to gather a basic understanding of the problem first and have a strong motivation and also see how this field of science evolved over time and how it started. You definitely reached your goal to make me think and dive deeper.
Un Super Grand merci! This series of lectures was very informative and clear - especially for someone who is not in the Historical field. The concepts were clear and very transferrable into my area of marine ecology :D fingers crossed I get to try this out!
It was a really informative and useful video series. Thanks a lot for that. However, as a novice person in this field, I realized the series was more focused on "Visualization" rather than "Analysis". Hence, I think the title of the videos can be a bit misleading.
Hi, thank you for your feedback. You're right, this video series was initially meant to be used in a conference focused on history (HNR), a discipline that relies heavily on visualisation for network interpretation. The title made sense in this context, and this is by the way only an "introduction": I find always easier to use the visual approach to make people that are completely new to these methods understand what network analysis is (and then move to some more technical things on metrics, analysis, etc.).
![Introduction to Social Network Analysis [2/5]: The Origins](http://i.ytimg.com/vi/hlAwvj60MI4/mqdefault.jpg)
![Introduction to Social Network Analysis [2/5]: The Origins](/img/tr.png)
![Introduction to Social Network Analysis [3/5]: Historical Applications](http://i.ytimg.com/vi/BQWoMRS5CGA/mqdefault.jpg)
What a great introductory course. Thank you so much!
Great touching on temporality. A network is a snapshot frozen in time. If you think of Time as an emergent physical property, not as a ticking clock, you can see a network evolves say through wiring and rewiring of nodes as they age and die at the experimental or theoretical rate you give them. Here differential and difference equations become handy for describing time equations for each node.
Thank you so much Martin Grandjean for this great introductory series. The graphics were beautiful and really helped to emphasize and understand the points you made. I think it's really important to gather a basic understanding of the problem first and have a strong motivation and also see how this field of science evolved over time and how it started. You definitely reached your goal to make me think and dive deeper.
Many thanks for your nice feedback! Cool, I hope you'll succeed in your deeper dive and your network analysis projects
Interesting - still above my head but can see the real world application and its uses.
Un Super Grand merci! This series of lectures was very informative and clear - especially for someone who is not in the Historical field. The concepts were clear and very transferrable into my area of marine ecology :D fingers crossed I get to try this out!
Merci ! Good luck for the application to your field (yes, the basics are valid for all disciplines)
What software would you use for creating a multi-layered network?
where can i look for more information about this?
It was a really informative and useful video series. Thanks a lot for that. However, as a novice person in this field, I realized the series was more focused on "Visualization" rather than "Analysis". Hence, I think the title of the videos can be a bit misleading.
Hi, thank you for your feedback. You're right, this video series was initially meant to be used in a conference focused on history (HNR), a discipline that relies heavily on visualisation for network interpretation. The title made sense in this context, and this is by the way only an "introduction": I find always easier to use the visual approach to make people that are completely new to these methods understand what network analysis is (and then move to some more technical things on metrics, analysis, etc.).