Dunbar's Number is a well-known concept which states that the number of social ties a person has is about 150. But... when Dunbar, the famous researcher who came up with this thesis, promoted this idea on his many interviews lately, people's reaction was of disbelief. 150 sounded to them as an inflated number. If it is true, where are all these friends? So who's right - Dunbar or the talkbackers?

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To make Network Analysis accessible, we'll learn about a free and popular Network Analysis Software called Gephi. In this episode, Gephi's founder, Mathieu Jacomy, will reveal new features and make you an offer you can't refuse if you're a Java developer 😊. In the next episode, we'll continue this survey with about a dozen more free Network Analysis Software. To help expand the SNA and Network Science community, NETfrix needs your ratings! Rate, review or tag NETfrix on Facebook/Twitter/Linkedin/Instagram. The most creative reviews/posts will be read in the following episodes.

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This episode features Dr. Ann McCranie, the Chief Organizer, with special tips to newcomers & Prof. Baruch Barzel on his lab's presentation about new discoveries & mysteries of Network Dynamics. Transcript & Links: http://bit.ly/NETfrix_Networks2021Blog Rate NETfrix: http://bit.ly/Rate_Netfrix Follow on Twitter: https://bit.ly/netfrixP_Twitter

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This episode is dedicated to improving our intuitions about networks, using the naive idea of 6-Degrees-of-separation and flip it (literally). On the way, we'll learn some new network laws.

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Network Science and its dear friend, Data, are here to throw some of our intuitions about politics and partisanship under the nearest bus. Partisanship is Networks' nemesis: Networks are all about connecting while partisanship hacks away at the network's links… So, who shall prevail? SUBSCRIBE or review here: http://bit.ly/NETfrix_Review Transcripts are available on SNApod.net See you on the other side of NETfrix.

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Network Science is an emerging & exciting field in Data Science and it brought to you by an emerging & exciting podcast. BUT be careful! Because with the great power that network analysis can give you - comes great responsibility.

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Here we'll discuss why dynamic networks are chaotic and what are the implications and applications of this statement, especially, to the field of machine learning.   PLS review here: http://bit.ly/NETfrix_Review Transcripts are available on SNApod.net See you on the other side of NETfrix.

Dynamic networks are data science's Holy Grail. Here we'll learn why, and what are the applications and implications of dynamic networks.  To do so, we'll have to understand what it has to do with complex system, chaos theory and, of course, groundhogs. PLS review here: http://bit.ly/NETfrix_Review Transcripts are available on SNApod.net See you on the other side of NETfrix.

How to tell a story about data by using Community Detection in networks. Communities (or clusters) are the building blocks of all networks. In this episode we'll learn about their formation and special features, and how to apply this knowledge on Big Data. And I promise - your views about cauliflowers will never be the same.  PLS review here: http://bit.ly/NETfrix_Review Transcripts are available on SNApod.net See you on the other side of NETfrix.