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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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Preparing your dataset for network analysis: a general introduction

Last week, your beloved Data Ninjas helped organize a workshop on how to get started with databases at the HNR2014 conference in Ghent. Due to popular demand, we’ll summarize the basic principles here. There’s nothing really SNA’y about building a database of course, but before you can go crazy with SNA, you have to have your data organized, so this is pretty important if you want to get something out of it easily. We’ve never really given this much thought actually: we’re pretty spoiled in this respect, because the database we work with, Trismegistos, was practically handed to us on a silver platter. Well, to Silke at least, two years ago. I actually helped build a large part of it for my PhD research, resulting in nightmares, insomnia and eventually a temporary ban on parsing names. Oh, those were the days… But we realize that others are not so fortunate and have to build theirs from scratch, which prompted our inner Mother Teresas to spread our miraculous database transubstantiation skillz among our peeps. For starters, we use Filemaker, cuz, well, that’s what Trismegistos rolls with. It’s really easy to work with: in fact, when you create a new database (File > New database; see: it’s that easy!), you automatically enter ‘layout mode’ and a ‘field picker’ pops up, in which you can start creating a list of new fields, which you can then drag to your new database (fig. 1 & 2).    Fig. 1: Filemaker ‘field picker’   Fig.…

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Multiple edge types in Gephi

Ok, I know it’s summer, most of you are taking a well-deserved break, and there are a lot more fun things to do than dealing with networks. For those of you nodding fervently in agreement: shame on you! A plague on all your houses! Except Gryffindor, I’m in that one. And no, I didn’t just pick that one because Harry Potter‘s in it. I got sorted, the proper way. At Pottermore.com. You should really try it, it’s so much fun!   Anywho, I recently got a cry for help concerning Gephi, and I thought: there may be more lost souls out there, struggling to get this right. So here’s the pickle: can you visualize different types of edges in Gephi?   Not all your relationships are the same: you don’t hang with yo brudda of da same mudda in the same way as you do with your bros, and you don’t treat them the same as your hos. Like chicks before dicks, ya know? This was already true 100, 200, 500, even 1,000 years ago. People had family (through descent as well as marriage: sometimes it’s useful to distinguish between the two), friends, colleagues, superiors, inferiors, extraterrestrial acquaintances, … . Or you might want to look at different kinds of interactions between individuals: who writes/ lends money/ sells a slave/ … to whom, whatever, you name it! Although you’ll probably end up filtering your networks according to specific types of relations anyway, it’s not a bad idea to start out with the complete picture, to…

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If you insist… more tutorials on R

This post is for all you nutcases out there who are considering working with R. Don’t be tempted by the Dark Side! They do NOT have cookies, however much they advertise the opposite. Only cookie monster has cookies, and he is obviously a UCINET guy. Because he’s blue, you see. And the UCINET website has lots of blue. Pure logic. Still sticking to R? Meh, well, it’s your life, waste away. Luckily, I got some tutorials to get you going. Just to be clear: these aren’t SNA-geared tutorials, just for R in general, but since you need a basic knowledge of how it works, this will definitely help. The first one is just the way we like it: simple and fun. It revolves around cats. Yes, cats. The internet is all about cats these days, and now they’re taking over R as well. Ok, gotta admit, R scores some serious points with this one. Get your catisfaction here. The second tutorial is a little more advanced, and explains how to write your own packages from scratch. If your going to be reusing functions often, this might come in handy. No cats involved in this one though, too bad 🙁 Finally, some reminders: – the CfP deadline for the Historical Network Research conference in Ghent in September is approaching (May 10). Make sure to sign up for our meet-and-greet! Check out one of our previous posts for more info. – I don’t think I’ve mentioned this one before, but there’s a…

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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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