Introduction to Social Network Analysis [1/5]: Main Concepts

Workshop by Martin Grandjean (Université de Lausanne) at the Conference HNR+ResHist2021 Conference "Historical Networks - Réseaux Historiques - Historische Netzwerke co-organised by HNR and ResHist.
The script is available here: doi.org/10.5281/zenodo.5083036
Chinese subtitles provided by the Hong Kong Baptist University Library - Digital Scholarship & Scholarly Communication Services (digital.lib.hkbu.edu.hk/)
This workshop, intended for a beginner audience, proposes to review the main concepts of social network analysis (terminology, visual analysis, centrality measures, communities, etc.) while highlighting the challenges that arise when analyzing relational historical objects. After an introduction to the basics of network analysis, we will provide an overview of the application of these methods in the historical sciences: there is no single way to extract a network from historical sources and this has implications for the types of analysis that can be done, sometimes creating biases that we must be aware of. And what is generally the status of network analysis in such research, is it a heuristic tool or just an illustration?
We will then look at the question of „translating“ the concepts and results of network analysis into a historical research. For example: how should betweenness centrality be interpreted? should community detection be used? can I trust the clusters that appear in the visual analysis?
To conclude, we will propose some reflections on the main difficulties of historical network analysis, between temporality analysis and multilevel system modeling.
hnr2021.historicalnetworkresea...

[@-Skywalker01-]

Very clear, good visualisation, right speed, thank you!

[@SMajid--SMajid]

This is a great explanation of complicated concepts. Many others have complicated them further, thanks so much for such clear examples and simplification of concepts!

[@prernamistri]

wonderful video !

[@reijin999]

excellent video thank you

[@rezat.ashtiani1338]

Excellent and simple, thanks

[@MartinGrandjean]

Many thanks!

[@muskduh]

this is great! thanks for building bridges for us

[@MartinGrandjean]

Thank you, I'm happy if it can be helpful!

[@murilopalomosebilla2999]

Really well presented! Thanks!!

[@MartinGrandjean]

Thank you for the feedback!

[@rodrigodasilva9176]

Very interesting subject. Very good explanation. Is there a business application to this ? Myb help product creators/sellers to understand where their audience is and how they are linked throughout social media.

[@anapauladonate]

Congratulations!

[@jenS.283]

thank you very much

[@victorias7324]

Good explanation, thank you

[@MartinGrandjean]

Thank you!

[@movimientoinformativo5314]

excellent

[@mghamari63]

Perfect presentation! I am pretty new in this area. Two questions: A:What does distance mean in a network and how it is measured? B: How can we read a complex network? I mean is there always a matrix (matrices) behind every galaxy-form network like what you showed at 10:50? Thank you!

[@MartinGrandjean]

Thank you for your questions. In a network, the distance refers to the number of nodes, that you need to go through from node A to node B (think about a metro map where you count the number of stops), the visual distance between the nodes in the visualisation is not meaningful, it's just the result of the spatialisation algorithm. And yes, there's alway a matrix behind a graph, even a very large one, but you'd often simplify it as an adjacency list because the adjacency matrix contains looots of empty cells (you rarely have a graph where all pairs of nodes are connected).

[@mghamari63]

@@MartinGrandjean Thank you for your reply! Can I have you email address please?

[@mghamari63]

@@MartinGrandjean How can we evaluate the accuracy of an inferred network? or How do we know that the constructed network accurately representing the interactions between entities? Thank you!

[@MartinGrandjean]

@@mghamari63 I don't think I'm talking about inferred networks in this video. It's something that's intimately related to the discipline, the type of data, and the specific situation, so there's no general answer to that question. I feel like you have to compare it to other networks of the same type, or to a representative sample. But in history (which is the context of this video), we rarely use networks that aren't exactly the data we have (this has biases, but at least we know exactly what we're talking about).

[@user-yn9vp4xn1o]

UNDERSTANDING ME UNDERSTANDING YOU

[@AA-bs1ig]

Hi , please how not create a redundunt path between 2 nodes already has a path.

[@adrianmaulanamuhammad7225]

Do you have a reference or source where the metrics are fully explained? I still have questions, like are we need to calculate all the metrics (avg path length, diameter, avg degree, etc.) or we can caculate a few metrics? How many metrics are enough to represent a network? Thanks

[@mghamari63]

@@MartinGrandjean The link of "Translating Networks" does not work. Would you share it again? Thank you!

[@MartinGrandjean]

@@mghamari63 Sorry for checking the comments this late, we’ll in fact KZbin added the ) at the end of the URL as if it was part of the link. I just removed it and think I works now.

[@user-jl6tl6tt8d]

Hi, nice presentation. Can you tell me, what font did you use on your document?

[@MartinGrandjean]

It's Optima :)