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Category: Tutorials

Spaghetti Monsters al Dente

Cowabunga! Your beloved Dataninjas strike again! A couple of weeks back, we were invited to give people a hands on experience of how to make their own squirmy, noodly contraptions in Gephi at the ‘Papyri and Social Networks’ conference in Leiden. We definitely won over a soul or two for our cause, and for those who were still struggling to see the light, we promised to provide our presentations so they could let it sink in a little more. And so today we would like to share the joys and sorrows that come with network building in Gephi with the rest of the world. You can download the files here: general introduction gathering and structuring data Gephi tutorial 1 Gephi tutorial 2 No doubt many of you, in a fit of insanity, have accidentally deleted the sample files we provided to work with during the workshop. Since ninjas are badass, but not bad, we’ve decided to let this one pass with a slight contemptuous smirk, and add them here as well: so here’s the nodelist and the edgelist (right click to download these, otherwise they’ll just open up in a separate window). Now knock yourself out! Yanne & Silke

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Gephi timeline: the pimped out version

Random introduction (no Gephi timeline) Ok, finally got over mourning Dumbledore a little, and I have to confess: the new James Bond trailer helped BIG TIME. I’ve gotmixed feelings about this one though. Well, just one major bummed feeling, actually. Because this is probably Daniel Craig’s last Bond movie. But! But, but, butt! Booty. Ass. Derrière. Cheeks. Fart. Eine feuchter Furz. There. Now I can finally use that expression. So, back to Bond (I bet his farts smell like expensive cologne. Not sure if that’s a good thing though). If you think about it, this is going to be THE ultimate movie. Now, I don’t know if many people who saw the last one really grasped what was going on there. Everyone was so focused on Silva and how in the end Bond fails and M gets killed anyway (oops, perhaps I should have added a ‘spoiler alert’ at the beginning again…), that they missed the developments that really matter. But, as is so often the case, Silva was actually just another straw puppet, a minuscule link in a master plan genially devised by no other than our very worst enemy and nightmare: VOLDEMORT. You fools! He-Who-Must-Not-Be-Named was not killed by Harry Potter! ‘But he burst into little bits of charred paper at the end of the last movie’. Balderdash! Voldemort isn’t stupid! With all his Horcruxes destroyed and a jittery Elder Wand, he knew it would be reckless to face Harry again. So he did a neat disapparating trick…

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Gephi Timeline Basics

*SPOILER ALERT* Don’t read this if you haven’t read Harry Potter yet! But in that case: shame on you! My dearest Kartoffelköpfchens, These last months, this jetsetter has been travelling the world, spreading the word of our spaghettilicous Overlord, from Paris to Padua to Florence (Ok, I admit, that’s not really impressive on a global scale… But you can’t blame me for all those trips to Italy: it’s pasta heaven over here!). But with summer almost over, I think it’s time to remind you beach bums and party addicts that there’s more to life than tans, summer flings and alcohol. Networks in particular, of course, cuz that’s what we’re here for. A while back, a waaaay big whale of a while back (I saw a whale once. On a boat trip off the coast of Boston. Was sick to the bones. Two weeks ago I saw a dolphin. On a boat trip off the coast of Ireland. Was sick to the bones. Coincidence? Yeah, why not), I told you I was playing around with Gephi’s timeline feature. We’ll, today I’m gonna show you how that works, because it’s an indispensible feature for any historian who’s into serious network analysis.   Gephi Timeline Basics To activate Gephi’s timeline, you need a nodelist and an edgelist, just like for a static network (if you’re not sure how that works anymore, check out this post on the basics of Gephi). All you need to do actually, is add a time interval to both.…

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Sigma.js export plugin for Gephi

Dear tater tots, It is with utmost joy, enlightened rapture, and a bar of celebratory Belgian chocolate in my hand that I write this post today. For the past ten days, and the following eleven weeks I have banished myself to a quaint little Dutch canal town called Leiden to get a taste of ‘international mobility’ and ‘internationalization’, of which the academic world is so very fond. Now, I don’t mind peeking over hedges and borders once in a while, as long as the curtains remain drawn in crucial places. This way, I found out that my ivy was growing into my next-door neighbor’s bathroom, who had left for prison a couple of years before and forgot to close the window on his way out. Not knowing what he was charged for, I rather didn’t take any risks and removed all traces of this floral intrusion immediately. On another occasion, while coming home from work on a not so particularly hot and humid day, I happened to look into a living room a couple of doors away, to find its owner spread out on the couch in front of the window, with nothing wrapped around his chunky frame but a mucky pair of undies, a grimy tank top even Onslow would shy away from, one hand clasped around a XL can of beer, the other lazily scratching his balls, giving me a disgusting grin when he noticed my intrusive glance. Would it kill to get a pair of G-Stars for…

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DataNinjas = read.csv(“software.csv”, head=T, sep=”;”, na.strings=na) (3)

[Recap: There’s tons of software out there to help you with your calculations, correlations, transformations, permutations, visualizations, … (here we go with the –ations again! Seriously! Maybe I should make a network out of them). There are all-rounders, like UCINET, some focus more on the numbers (such as R), others (Gephi for example – this post is exactly about that: a Gephi Tutorial) are geared toward those who like fancy spaghetti monsters (guess who?!). If you’re working with really large, or even HUGE data, Pajek’s your cup of tea, although it works just as well for small networks. There’s no such thing as “the best” program to work with, although everyone probably has a favorite. Gephi is our top choice, not just because of the fancy schmancy visuals, but also because it’s very user-friendly. When it comes to metrics, however, it’s pretty limited. For those, I turn to UCINET, while Silke gets her kicks in R. Why? No idea. The fact that she took the introduction course to SNA & R and I the equivalent for UCINET at last year’s Sunbelt in Hamburg has nothing to do with this of course. Anywho, over the next couple of posts we’ll let you know what we think of some of the major software programs, with tips ‘n tricks to help you out. If you have any questions, Google it, for cryin’ out loud, that brain of yours is there for a reason! Just kidding, we’re happy to help if we can. Not.…

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DataNinjas = read.csv(“software.csv”, head=T, sep=”;”, na.strings=na) (2)

[Recap: There’s tons of software out there to help you with your calculations, correlations, transformations, permutations, visualizations, … (here we go with the –ations again! Seriously! Maybe I should make a network out of them). There are all-rounders, like UCINET, some focus more on the numbers (such as R), others (Gephi for example) are geared toward those who like fancy spaghetti monsters (guess who?!). If you’re working with really large, or even HUGE data, Pajek’s your cup of tea, although it works just as well for small networks. There’s no such thing as “the best” program to work with, although everyone probably has a favorite. Gephi is our top choice, not just because of the fancy schmancy visuals, but also because it’s very user-friendly. When it comes to metrics, however, it’s pretty limited. For those, I turn to UCINET, while Silke gets her kicks in R (which is why she’s doing the R tutorial). Why? No idea. The fact that she took the introduction course to SNA & R and I the equivalent for UCINET at last year’s Sunbelt in Hamburg has nothing to do with this of course. Anywho, over the next couple of posts we’ll let you know what we think of some of the major software programs, with tips ‘n tricks to help you out. If you have any questions, Google it, for cryin’ out loud, that brain of yours is there for a reason! Just kidding, we’re happy to help if we can. Not. No really,…

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DataNinjas = read.csv(“software.csv”, head=T, sep=”;”, na.strings=na) (1)

So, now you’ve caught up with the basics of SNA you’re just dying to try it yourself, right?! Alright! Now, the next step is finding the right software. BE WARNED though! If you’re thinking: ‘Yes! I’ll just dump my data in the first program they mention, tap a few buttons and all secrets will be revealed’, you’re seriously mistaken. The computer does not solve your research questions for you. If it were that easy, we’d all be out on the streets begging for a job. You can let the computer perform all the hocus pocus you want, YOU are still going to have to interpret the results. There, now we scared of the SNA posers, we can get down to business. UCINET Tutorial: Why UCINET? There’s tons of software out there to help you with your calculations, correlations, transformations, permutations, visualizations, … (here we go with the –ations again! Seriously! Maybe I should make a network out of them). There are all-rounders, like UCINET, some focus more on the numbers (such as R), others (Gephi for example) are geared toward those who like fancy spaghetti monsters (guess who?!). If you’re working with really large, or even HUGE data, Pajek’s your cup of tea, although it works just as well for small networks. There’s no such thing as “the best” program to work with, although everyone probably has a favorite. Gephi is our top choice, not just because of the fancy schmancy visuals, but also because it’s very user-friendly. When it comes…

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Social Network Analysis for Dummies

What’s that? You want us to tell you all about nodes, edges and SNA metrics? A veritable ‘Social Network Analysis for Dummies’? You got it! But before we take you on our little ‘intellectual’ journey, let’s get caffeinated and make ourselves comfortable. Yes, that’s an order! So put your feet up! (if you’re at work: colleagues make great footrests – you’re welcome) If at this point you’ve made yourself a fort of cushions, you’re my hero. Anyway, anyway, time to get down to business! Social Network Analysis for Dummies: Part 1 First of all, what is Social Network Analysis (SNA) all about? As with everything, there are many different definitions floating about but we’ve tried to keep it simple and clear for you. SNA is a quantitative and qualitative analysis of a social network. So you start with a social network, which is a structure made up of actors/entities (such as people, companies, whatever you want!) and their relationships. What happens to an actor is dictated by his position and the structure of his connections: this will determine which information or resources will (or will not) reach him and will therefore influence his behaviour or beliefs. These entities are called the nodes or vertices of a network. The relationships are what we call the ties or edges of a network. Matrices and graphs can be used to visualize this structure. A matrix sounds like a pretty scary thing, but not to worry! It’s actually quite a straightforward arrangement of our…

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