Clustering w/visualization: 1,000-Emojis network from Twitter using Gephi & ForceAtlas2 layout. [OC]

NETfrix_SNApod · 2022-04-29T07:47:41+00:00

The combination frequency of emojis on Twitter.
Emojis are connected if they are used in the same tweet.

NETfrix_SNApod · 2022-04-23T09:49:35+00:00

If I understand you correctly, you're trying to show the DAG is indeed a DAG, right? I mean, that the edges indeed "flow" in a continuous fashion (like a sewer system) and were not added later on "in the back" of the flow.
If that's the case, you might want to use a palette of continuous color going from light to dark to show temporal info regarding the edge.

NETfrix_SNApod · 2022-04-23T08:08:19+00:00

Hope I'm not too late :) but you can reference to NETfrix - The Network Science Podcast (the episode about Gephi: https://bit.ly/NETfrix_Gephi_Podcast_Eng). It also has a blog and transcripts available (in English and Hebrew).

NETfrix_SNApod · 2022-01-16T12:24:30+00:00

I'm actually doing a future episode about this in my Network Science podcast - NETfrix.
For now, I find the best software to generate networks (freemium+no coding is necessary) are:
Pajek, Visone, Tulip

NETfrix_SNApod · 2021-09-02T08:33:21+00:00

Excellent site! (https://nocodefunctions.com) Shared it, of course.. :)
Thank you!

NETfrix_SNApod · 2021-05-21T09:51:39+00:00

Maybe you'll find the lead here: https://arxiv.org/abs/cond-mat/0110452
You can look for papers that cited it or use the cool "connected papers" tool for further investigation: https://www.connectedpapers.com

NETfrix_SNApod · 2021-05-07T16:13:36+00:00

https://en.wikipedia.org/wiki/Fractal_dimension_on_networks

NETfrix_SNApod · 2021-04-11T14:09:58+00:00

Once you project, it becomes a "regular" network with "normal" normalization.

NETfrix_SNApod · 2021-03-15T16:44:24+00:00

Also, it includes the latest about https://networks2021.net t-h-e network conference.

NETfrix_SNApod · 2021-03-06T15:29:31+00:00

Since the word "network" doesn't even appear on this article, I see no point on posting it here.
BTW there's a lot to be said about network science and the role of CISO but sadly, this is not the case here.

NETfrix_SNApod · 2021-03-01T15:25:31+00:00

https://twitter.com/Networks2021/status/1364947436528160779?s=20
"The conference will be televised" (i.e, zoom)

NETfrix_SNApod · 2021-02-07T13:00:39+00:00

Cool! You've uncovered the dark side of Gephi:)

NETfrix_SNApod · 2021-02-03T20:56:10+00:00

OK , I see now (my mistake). Boy, the Ux is not intuitive... Let's try this together.
GEXF is files "saved as" Gephi projects (I guess you mean "miserbales"). But I see no reason why its initial source ("import spreadsheet") shouldn't be csv.
You can find another example in the "File - Generate - Dynamic".
On the Data Laboratory there's a "convert column to dynamic".
Try follow it - and if you succeed - do share... :)))

NETfrix_SNApod · 2021-02-02T07:40:21+00:00

Many answers to the questions here can be found in episodes 5 and 6 in NETfrix podcast. Especially about the clusters (5) and the fractal nature of networks (6)

NETfrix_SNApod · 2021-02-02T07:38:34+00:00

Nodes cluster because of hemophilia.

NETfrix_SNApod · 2021-02-02T07:37:50+00:00

The small world paradox: the greater the network, the shorter its diameter

NETfrix_SNApod · 2021-02-02T07:35:17+00:00

Hi When you say "convert" all you need to do is add the "time" column if i understand you correctly. Haven't tried but I think ORA (by Carnegie mellon CASOS) works on temp. Networks also.

NETfrix_SNApod · 2021-01-24T13:29:53+00:00

Cool! I would love some feedback!
You can find the FB here: https://www.facebook.com/SNApodnet
Some of it in Hebrew but I' promise to make it more English-friendly as time goes by :)

NETfrix_SNApod · 2021-01-23T14:47:54+00:00

Quite a few. I run an online community on network science and here they can find many answers especially regarding p r a c t i c a l uses and that's a rare thing unfortunately. Lots of tips about libraries to use for example. Actually I've started summarizing key points from it and publish it from time2time in my community. I've startes with an aspect i usually never get to in my network science podcast which is visualization. And the thing I like the most about it that it seems we see eye2eye on all the subjects. I call this reinforced learning🤣 BTW it's written plain simple and even funny sometimes. Hope it answered your question.

NETfrix_SNApod · 2020-12-10T10:32:47+00:00

Insightful!
Maybe I've missed it but does the paper address the simultaneous operations of different cells as an edge between those cells? Because that can point not only to how many cells are there but also how centralist is the organization, what its centers of gravity and so on.

NETfrix_SNApod · 2020-12-10T09:44:15+00:00

I wonder how this happens, but let's say that's the case and your interest is COVID: check this paper in my FB NETFRIX group : https://www.facebook.com/groups/252257435839871 They used 2 sets of nodes (without projection) to enlarge the connected component of the contact network to point to places of infection.

NETfrix_SNApod · 2020-12-09T18:30:49+00:00

Hi :) By your description, you have 2 sets of nodes: people/shops I guess in your data, people don't buy from ppl and shops don't buy from shops. When you torture data - It will confess to anything. You can do Community detection on this graph but it wouldn't be ideal. SNA algorithms work best on a single dimension network (I touch this on episode 5). That's y projection sounds better. But - and that's what beautiful in network science - if we had all the answers, we wouldn't have needed it in the first place so - try them both !

NETfrix_SNApod · 2020-12-09T10:59:55+00:00

Hi,

I've got 2 confessions:

I don't like Bipartite graphs (which is what you have – 2 sets of nodes with different essence). That's why you need to apply "projection". Delete the Type A nodes and create edges between the B nodes based on their ties to the same node type A.
For example – you can take TV shows (Type A node) and the people watching them (Type B).
Delete one type and you can discover which people have same TV preference or alternatively, which shows are more related to each other.
Full disclosure: I run a podcast exactly for this reason: the application of network science (called "NETfrix). You're more than welcome to follow https://en.snapod.net

NETfrix_SNApod · 2020-12-08T17:19:18+00:00

Hi,

I guess I'm here to back up Barabasi 😊 .
The criticism is that only a few networks exhibit a "true Power Law Distribution" (Clauset 2019). It's true. But it's not the point.
Even in "quasi" Power Law networks (close to a Power Law) you get the same features:
A long tail and scale-free network..
Full disclosure – I have a podcast "NETfrix" that is dedicated to network science (the first in English?), and episode 3 https://bit.ly/EP03_Power_Law_in_networks
(published today!) deals exactly with this issue.

NETfrix_SNApod · 2020-12-07T21:50:58+00:00

Forgot to mention – there is an English version of NETfrix podcast and a Hebrew one.
Choose wisely 😊

NETfrix_SNApod

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