…and then the world

“where nothing we’ve actually seen has been mapped or outlined…”

Archive for the ‘new tools’ Category

several dots on a map

with 10 comments

2010 is already looking like it’ll be fairly busy, not least because nearly a quarter of it is gone already. Over the next twelve months, I should finish my thesis, while other projects are also being developed and carried out: I’m tutoring in a first-year unit this semester, and am currently writing up new work on the French political blog research, first outlined at IR10 last year, for both my thesis and a conference presentation.

That presentation will be in June, at the International Communication Association conference in Singapore, as a paper co-authored with Lars Kirchhoff and Thomas Nicolai from Sociomantic Labs in Germany. Where my IR10 presentation looked at the text content of blog posts, this paper will be covering the links being made, in their various guises.

As part of this work, and indeed in preparation for research into topical networks, the links made around particular events or themes, I’ve been busy looking into the more permanent/static networks created by blogroll links from sites in the sample population. As with the IR10 work, I’m using data collected by Thomas Nicolai and Lars Kirchhoff over the first eight months of 2009, with 217 political blogs, media resources, and other related websites represented in the final collected data. For this stage, I’ve taken these sites as a starting point, making a list of each blogroll out-link from each of the 217 sites as a two-column spreadsheet (host site, site linked to), and then importing the final list into Gephi for visualisation purposes.

[Because I was using a slightly older version of Gephi, I was also converting the spreadsheet into Pajek’s .net format in order to import it into Gephi using Excel 2 Pajek. However, the latest version of Gephi imports .csv, with extra import options through the .gdf format too]

Having not used Gephi before (I couldn’t get it to work when I tested out visualisation options quite a long time ago), my success in testing it out was greatly aided by the Gephi team releasing a step-by-step tutorial for new users. Importing every individual link originating from the 217 sites and following each tutorial step led to something that looks rather spectacular, although doesn’t really say much:

here comes sciencey

Of course, the risk with visualisation is that too much attention is spent on the ‘pretty’ side of things, or on preparing diagrams that look impressive (or ‘sciencey’), but don’t aid the research’s argument (or even confuse it further). While the initial aim of creating a blogroll network is to help me see the groups of sites that associate with each other, trying to get a handle on how these sites in the sample relate to each other, the warnings and advice from people such as Bernie Hogan at last year’s OII Summer Doctoral Programme have stayed in the back of my mind. As such, I’ve spent a fair amount of time over the last few weeks trying to clean up the data and improve the visualisations, not from an aesthetic point of view, but so I get a clearer sense of what I’m trying to describe.

here comes sciencey (part two)

With the full list of links containing over 5000 nodes, receiving at least one in-link from one of the 217 initial sites, one of the main problems in the first visualisation is the sheer number of nodes, and the implied overimportance of sites with many out-links (especially when these sites are the only ones linking to many nodes – it leads to large groups of satellites around nodes). The next step then, as seen above, was to restrict the nodes to those sites receiving two or more in-links from the initial 217 sites. A number of loose groupings were immediately apparent (see, for example, the top-left of the diagram), and these were followed up after the next round of cleaning the data:

here comes sciencey (5b)

here comes sciencey (part five)

In the first of these two visualisations, some nodes are coloured by their affiliation to particular political parties (either by being official sites or by containing the party name/acronym in their URL). A loose grouping of sites from the Front National (brown) and UMP (blue) in particular is apparent. In the second visualisation, I located sites that were members of three different blog communities or networks, organised around different themes or beliefs. Again, there is some loose grouping – unsurprising, considering this is a blogroll-oriented network, and often sites will have links either to the main page of the group or the other members in their blogrolls – but what is most interesting is the general location of the anti-Sarkozy group Les vigilants (in pink) between the left-wing and centrist party groupings (in the first of the two visualisations). For more details and visualisations-in-progress, check out my Flickr (and look out for updates on the related paper over the next few months!). The next important step, particularly in terms of new information, is comparing the blogroll links to the topical networks, and seeing whether the same associations are in play regardless of time or topic – this will be investigated further over the next few weeks. At this stage, in particular because of its ease of use (and not being restricted to the latest version of operating system-specific software, I’ll most likely continue to work with Gephi while I work on my thesis. I’d still like to try out Prefuse though at some point, but that may have to wait until after all this work is out of the way…

Written by Tim

19 March, 2010 at 3:46 pm

well I’ll be Bertied: Perth as meme

with 4 comments

A first attempt at an experiment, and not a particularly rigorous one at that, in tracking information flows through Twitter.

On Monday afternoon (31 August), Australian-time, a new YouTube video was publicised*. There’s nothing particularly unusual in that, except that this particular video concerned Perth. The capital city of Western Australia, Perth is both extremely isolated and not always seen as the most exciting of places – being often scathingly referred to using terms such as ‘Dullsville’. So, when a three-minute video mocking aspects of Perth life and making up other information (possibly qualifying as what John Hartley describes as silly citizenship, but that’s for another time), hit YouTube, it quickly spread through Twitter, Facebook, and into the blogosphere, as Perth locals and expats (of which I am one) became aware of it.

Before going further, this is the video, made by Vincenzo Perrella and Dan Osborn and entitled This is Perth:

So, this gently mocking, amusing video was made, people watched it, told their friends. This can be tracked anecdotally; my personal experience of the video started at around 5pm Brisbane-time (all times from now on will be Brisbane time, despite this concerning Perth data – what I grabbed from Twitter was in my local time, and I did not want to overcomplicate things by starting to change times, especially since I was manually collecting the data. For Perth time, subtract two hours from Brisbane time), when Tama re-tweeted the link to the video. At this point, the RT was at least three steps down the line from its source, and the video itself was at around 350 views. Within a couple of hours, it had appeared three times on my friend feed in Facebook, within 24 hours it was up to 9000 views on YouTube, in 48 was well worth 35,000, and was at over 48,000 views at the time of writing. Links were also appearing in friends’ blog posts, and as the video spread, the media coverage grew too**. However, this isn’t the most precise or admissible form of measuring what had happened.

The most visible signs of people noticing the video and telling other people, at least from Brisbane, were through the likes of Twitter and Facebook. Searching Facebook for data was not the most successful of tasks, and indeed the variety of privacy settings can make finding content such as posted links hard to locate. Casually browsing livejournal posts and using blog search engines provided more results, but the re-tweeting activity on Twitter was the most immediately enticing option – it may be advantageous to return to the blogs and grab that data too, for comparison, but for now the only data source is Twitter.

The data set covering ‘This is Perth’-related tweets was obtained through multiple searches of Twitter, repeated over a couple of days to track new tweets. Without being as inclusive as possible, these searches attempted to locate as many tweets made between 31 August and 3 September linking to the video, discussing it or the articles on the West and PerthNow already covering it. Search terms included ‘This is Perth’, #thisisperth, and the various bit.ly and tinyurl addresses linking to the video, while further tweets were found by following the RT trails. The advantage of Twitter as opposed to Facebook was the prevalence of publicly accessible tweets; where locked posts were found, they were not included in the sample. However, if an RT included a user who had locked posts, the user was still included in the network created to show, where possible, the Twitter users acting as source nodes and hubs.

After the latest round of searches, carried out at 2pm Brisbane time, 227 tweets had been collected, not including those made by bots***. These had been made by, or took material from, 201 Twitter users. Of these users, 149 had specified a unique location, or made it apparent in their tweets – unsurprisingly, the majority of posts from which location could be determined came from Perth (92 tweets), with Sydney (16) and Melbourne (12) the next highest contributing cities. Outside of Australia, only nine tweets were from users declaring they were located internationally, with content being posted from the US, UK, Singapore, Canada, the Netherlands, and Malaysia. Such behaviour may be because of the localised nature of the video – for example, without knowing anything about Perth, the video may not be entertaining or interesting. Similarly, for people in or from Perth, seeing a video sending up their town may have meant some kind of connection with the video, and subsequently meant that it was passed on to friends, sharing the joke.

tiptweets

While geographically the mentions of the video were centred on Perth, time-wise the four hours after the video was first tweeted saw the highest activity; the earliest mention found in these searches was at 3.55pm on 31 August, with 25 additional tweets by 5pm and 41 between 5pm and 6pm. These coincided with the novelty of the video, spreading it when there was a good chance other people hadn’t seen it, and also with the end of the working day in Perth (peaking between 3pm and 4pm Perth-time). The WA-dominance of the coverage can be seen in the graph above. The graph depicts the number of tweets in hourly blocks, with the periods of little or no activity correspond with early hours of the morning, while the small increases in posting on Tuesday are during the work day and, in particular, the 7pm – 10pm period – however, these periods still contain less than 10 tweets an hour relating to the video. [The graph does not feature the last tweets from Wednesday night, when A Current Affair had a story on the video, as the exact time posted could not be determined, being in the format around 16 hours ago]

fullnetwork

While the video hits continued to increase over the period covered here, Twitter coverage died down quickly, with occasional flurries of re-tweets as people who had not seen it earlier discovered it and passed it on. However, the longest chains of re-tweets occured in the first hours of the Twitter activity. The network visualisation above shows each Twitter user (excluding bots) featured in the sample as a node. The visualisation uses directed edges – the connections are not necessarily reciprocal links between users, but show a one-way link from one source user to a second user who may have either directly replied to a tweet or re-tweeted the work of the first user. Many nodes are not connected to others, having posted once and not been re-tweeted or not discussing it further with other users (at least, in a way that the particular searches used here would have found). There are also several small groups of two or three nodes, showing one user responding to or re-tweeting the post of another user. Most notably, there is a large, connected system of nodes in the middle of the visualisation, and for the most part these are connections that were made, or build from those made, in the first few hours of the Twitter coverage.

main_network_nolabels

This closer look at the visualisation shows several paths for information flows, originating at a few source nodes. The longest paths contain nine nodes – starting at SixThousand, the Perth edition of a national network of subcultural e-newsletters and guides, re-tweets flow through people connected with The West Australian, and eventually crossed the country, reaching, for example, Fake Stephen Conroy, a popular Australian user satirising the Federal Communications Minister. To get to the end of these longest paths only took three hours from when SixThousand posted the first link – and by that point the number of tweets per hour covering the video was already declining.

The point of this exercise was not to claim anything about the nature of interpersonal communication using Twitter, or in Perth, or anything of that nature. For one thing, the data set is far too small to make any conclusions about information flows, while not looking at other data from additional sources such as Facebook or blogs means that a wider overview of the spread of the This is Perth video is lacking. Similarly, private communication such as email (the primary way I personally told friends about it) is not represented here. The main aim, instead, was to examine how to mine data from Twitter and what to do with it. The work here is a useful starting point for carrying out larger processes, ideally using automated tools such as NodeXL. One particular aspect I would have liked to cover here, and may do so later, is a comparison of the main connected group in the visualisation above and the actual followers of these users, whether what is depicted above shows information crossing groups or whether there is a high degree of interlinking amongst a group of friends.

In the meantime, what is shown is a short-lived burst of activity surrounding an amusing video about Perth, that quickly spread amongst a number of people either from or with connections to Perth, and then became a less prominent topic. While some coverage, such as last night’s A Current Affair story, and discussion of the video has appeared since the peak buzz surrounding it, activity hit a definite peak very early on – possibly reaching saturation point amongst a small audience? – and as the video itself has continued to gain hits, there just might not be any need to keep publicising it…

The network visualisation was made using GUESS, the graph through ManyEyes

* And possibly uploaded; the video’s page says 30 August, as opposed to 31, but there may be time difference issues.
** For example, stories posted on PerthNow and the West online, radio coverage on Nova 93.7, and a story on A Current Affair.
*** This may be a point of contention, as bots may be seen as further publicising the content and making it visible to more users, but for this initial work they have been excluded as the chain of re-tweets ended with them.

Written by Tim

3 September, 2009 at 6:25 pm

more links from the tubes

leave a comment »

A few things from around the traps that have come up recently (and have been noted elsewhere already!)

1. the 3rd International Conference on Weblogs and Social Media happened a few weeks ago in San Jose, California – going from the papers from last year and the provision of a dataset for people to use before submitting papers for this year’s conference, there may well be some interesting new work coming out of the proceedings. May try and get over to Washington D.C. for next year’s conference.

2. Sciences-Po in Paris unveiled their Medialab with presentations by Richard Rogers (govcom/issuecrawler), Yochai Benkler, the gephi team, and the webatlas team – with the rtgi group based out at Compiegne, north of Paris in Picardie, there’s a couple of exciting projects and labs taking shape in France at the moment.

3. Meanwhile, over at the Berman Center at Harvard, the I&D team have launched an interactive version of the Iranian blogosphere map documented in a paper released early last year. Haven’t had much time to test it out yet, but given the other international projects happening over that way at the moment and the linkfluence/rtgi-type projects, this kind of interactive, rather than static, output may become more common in blog and internet network analysis and mapping.

4. Speaking of maps and internet networks, there’s been a bit of coverage of the new map of social (network) dominance over at techcrunch. Obviously, the general dominance, in western countries at least, of facebook over allcomers is a major talking point, but it’s also worth comparing the map to that produced two years ago. Again, facebook’s spread is particularly evident, but whereas in 2007 myspace still had a majority, of whatever margin, of dominance in such countries as Australia, the US, Italy, and Greece, facebook usurping it in all four of those countries, as well as taking over most of western Europe and claiming a large chunk of Africa, leaves myspace’s sole outpost in 2009 as… Guam? The move of facebook into many languages has also meant that the previously language-specific clusters – such as skyblog’s control of francophone nations – is eroded. There’s more to be taken from both maps, and I haven’t looked at any of the numbers involved here – both maps use data from Alexa, but as noted in the Techcrunch post there’s some debate as to whether myspace or facebook are the leading social network in the US. However, I’ll leave it on one final, pleasing point – that the 2009 map, being zoomable and able to select and customise views, has been produced using ManyEyes (mentioned here many times previously).

Written by Tim

10 June, 2009 at 4:58 pm

linkfluence visualise the French blogosphere (or bits of it) (twice!)

with 3 comments

Previously mentioned on several occasions, linkfluence/rtgi, who are leading the way in not just visualising maps of online networks but also giving several levels of information and scalabity, have in the last week or so released two visualisations for different sites. Last year, of course, they produced PresidentialWatch08 for the US Presidential election, and in 2007 had Observatoire Presidentielle for the French equivalent. Now come two new maps, one blog-centric and the other providing a more topical view of website connections.

wikipole

First is the Wikiopole, for Wikio (a search and ranking site, who have also been developing tools for researchers, including their Backlink Factory). Depicting the connections between the top 1500 ranked blogs, and with sites coded based on their category (political, science, sport, etc), the map provides another overview of the state of the French blogosphere, this time in May 2009 (and may be useful if a map comes out every month/several months – in which case, archiving each edition would be rather handy). It’s also good to see visualisations not just looking at the political side of things (not that’s necessarily a bad thing, but there are plenty of ways to subdivide networks of blogs). Plus, as an overall blogosphere study, there’s scope to compare the statistical layout of the linkfluence map to the personal work from ouinon.net in 2007, despite the long period between the production of the particular maps.

toile_europeenne

The second map is for touteleurope.fr, looking at 2046 sites (not just blogs) discussing Europe(an politics) in French. There’s quite a bit of cross-over, understandably, between this map and the Observatoire Presidentielle, although it’s less concerned with the different political ideologies present and the types of site and separating the analysts from the ‘militants’, for example.

I’m on a rather slow internet connection at the moment (and unfortunately the two maps take a while to load for me), and still waiting for some information before looking further at the two maps – a lengthier write-up will come, but for the moment any new work in the French blogosphere, political or not, and in network studies and visualisations (even with reservations about methods or outputs, as the case may be) is welcome.

Written by Tim

6 May, 2009 at 6:31 pm

what to do with blog posts: another test

leave a comment »

With my confirmation seminar next week (Tuesday to be precise, more details on that later), I’ve been thinking about what I’m trying to get out of this research project, which bits of the data to study, and how these might be represented within my thesis (and any other outcomes). Because I am quite possibly insane, over the last two days I’ve grabbed (manually) the full text of each blog post made on Pineapple Party Time – a blog hosted on Crikey and run by Mark Bahnisch of Larvatus Prodeo, William Bowe of the Poll Bludger, and Possum (Scott Steel) from Pollytics. I chose this blog mainly because it had a brief, and complete, lifespan – it ran for a month, being launched on Tuesday 24 February 2009, when the Queensland state election was called, until Monday 23 March (two days after the election itself, enough time for a few final analyses). Of course, that didn’t mean there were only a few posts, around 130 in total (having copied and pasted each one into its own document), of which Bahnisch contributed the most.

So, with all the posts in raw(ish) text format (except for the election day liveblog – see below), and not worrying about links or comments just yet (I didn’t save comments, but I’ll probably get some graphs happening comparing number of posts per day and comments per day, both for the whole blog history and per author), what should be done with this data? Well, textual/content analysis of some description, but something quick would be preferable for the moment. I’m going to run everything through Leximancer a bit later, but earlier in the week ManyEyes (featured here previously) added a new data visualisation tool to its range of options: phrase net. This method allows you to upload your data set of many words and find common combinations of phrases along the lines of ‘x is y’, ‘x’s y’, ‘x of the y’, ‘x and y’, and so on. So, in the name of research, I’ve been testing it out. Here’s the visualisation (currently of the ‘x is y’ format) for posts from the entire blog:

ppt [ManyEyes]

Given the general themes of the election coverage – Premier Anna Bligh calling it early, the LNP looking to gain a big swing of voters away from the ALP, polls being seen as giving the LNP a slim victory or making the contest too close to call – some of the combinations showing up are unsurprising (‘Labor is worried/scared/vulnerable’ for example).

Going on an author by author basis, though, this changes a little, given Possum and Bowe’s focus on, for example, poll analysis and electoral data. Read the rest of this entry »

Written by Tim

26 March, 2009 at 12:17 pm

phrases of the blogosphere

leave a comment »

memetracker

Another visualisation of blog (and other media) data: MemeTracker provides an alternative to the likes of Blogpulse, tracking stories and events across the blogosphere and mainstream media online through the presence of key quotes and phrases. The resulting visualisation shows the popularity and also lifetime of a particular story – for example, the Obama quip “you can put lipstick on a pig”. Looking at quotes and phrases is a useful method – the political one-liner can pop up years after the story itself has been dealt with, haunting later politicians and administrations. Indeed, a thread over at Larvatus Prodeo has reminded me of John Howard’s “If I were running Al-Qaeda in Iraq I would put a circle around March 2008 and pray as many times as possible for a victory, not only for Obama but also for the Democrats” (reported, for example, on the 7.30 Report back in February 2007)…

MemeTracker also has a ranking of sites used in its data gathering, based on their response time to stories, whether they are ahead of the curve or not. The usual blog suspects, the likes of Huffington Post and Daily Kos, not to mention Drudge, are among the quickest at reporting stories containing the phrases being tracked, with the Huffington Post in particular featuring nearly three out of four tracked phrases. Australian news sites vary with their response rates. http://www.news.com.au is the quickest to report out of those I saw from a quick glance, two hours before the fairfax duo of the Sydney Morning Herald and the Age (and also news.com.au …), while theaustralian.news.com.au on average only covers stories with said phrases at their peak popularity… The news.com.au, SMH, Age coverage also feature over 50% of the phrases, compared to 34% for the Australian ABC and 30-50% for the majority of British news sites (news.bbc.co.uk, telegraph.co.uk, timesonline.co.uk) – although the Guardian, one of the earliest of the UK sites to report the phrases, gets up to 70% (possibly due to its blog integration and amount of online-specific content?). There are plenty of aspects of MemeTracker to still investigate – which sites are on the source list, which aren’t, particularly international blogs (as opposed to international news sites), as the phrases used are, understandably, US-centric, and whether the sites earlier to cover stories influence the coverage of the subsequent sites, but it’s another interesting approach to tracking political discussion online and visualising it.

[via information aesthetics]

Written by Tim

11 November, 2008 at 3:18 pm

let me see you

with 2 comments

A while ago, Sky asked me for suggestions for mapping/visualisation tools for one of her chapters, and she’s since been testing out IssueCrawler, which she discusses here. While writing a quick list of possible tools, I came across a couple of new visualisation programs that I hadn’t tested, so this morning is all about seeing what the various software and online tools can do.

For today’s experiments, I’m not using either IssueCrawler or ManyEyes – I’ve discussed both previously, anyway, and IssueCrawler is not actually useful in this context – I’ll try a second entry about crawls, scraping, and visualisation (the likes of IssueCrawler and VOSON) later this week, hopefully. For this post, though, I’m going to take data acquired by hand and put into a two-column spreadsheet in Excel (I know, I’m a terrible person for not doing it in Calc, but this will be relevant a little later). I’m using the spreadsheet I created manually from blogroll links of the Wikio.fr Top 100 French Political blogs in May 2008, rather than crawling the internet looking for connections. ManyEyes will be used as a reference, but as I’ve already visualised the data being used, I’m not going to redo that process today. I’m also not going to go through what the visualisations show from the data involved, but (however shallow this may be) I’m focussing more on the aesthetics, what the maps look like and how this can be customised, exported, and embedded.

For the purposes of comparison, here is the original ManyEyes visualisation of the blogroll links between blogs on the list of the top 100 French Political blogs (May 2008):

ManyEyes visualisation

To create the visualisation above from the data was straightforward, a simple select the relevant cells in the two columns, copy-paste, and let ManyEyes do the work. However, the customisation of the visualisation is an issue – the layout can be recomputed and the diagram embedded in other sites online, but any other changes are limited. So, in the interests of comparing tools, and the likelihood of working with other data types later on in my research, I looked for other resources.

NetMap visualisation test

There is an add-on for Excel (2007 only, though) called .NetMap, which allows users to generate network maps from their data (the standard Excel chart options don’t do this, and neither do those in Calc). After a bit of playing around with options and updates to get everything working, I generated the above visualisation. The display options are heavily customisable – from vertex colour and shape to edge colour and opacity – but, for some reason, as you can see in the screenshot, the vertex labels did not show up. This is fine when using .NetMap itself, as the diagram is next to the spreadsheet itself, and when you select a vertex, it shows the edges connecting it to other vertices and highlights the relevant cells in the spreadsheet. Beyond that context, though, such as when I use the screenshot elsewhere, there was important information missing (admittedly, my brief tests may have just overlooked some settings, as is possible with any of the programs discussed here). [Edit: Indeed, after a helpful email and a bit more playing around, I’ve managed to display the labels alongside the vertices. This is what you get from not thoroughly exploring all settings…] A more useful aspect of .NetMap is the ability to generate subgraph images; basically, each vertex’s individual map, ignoring all the vertices it is not connected to. However, as .NetMap only works at the moment with Excel 2007, and my computer is destined to take on a Linux flavour around Christmas time, .NetMap is not an ideal long-term option for my personal visualisation needs. Nevertheless, for my research it will still be useful, and it’ll still be running on my work computer.

Cytoscape visualisation test

The above visualisation was created using Cytoscape, which has so far worked ideally – again, I haven’t tested it thoroughly, but it also allows display customisation and a range of layout algorithms. Importantly, it also allows direct import of data from an Excel spreadsheet. In the program itself I haven’t quite worked out how to get more information displayed, but the resulting visualisation is very pleasing and clear. I will be using Cytoscape more often, I think.

UCInet (Netdraw) visualisation test

One of the reasons I chose to use the reduced blogroll list is the focussed nature of edges and vertices – the first spreadsheet, of nearly all blogroll links, has many vertices that are only connected to one blog, which created rather large, messy maps. In addition, it’s easy to compare these maps through their small sample size and the presence of the tiny ‘island’ of five blogs not connected to the main network. After the Cytoscape test, I moved onto the ‘big two’ programs for social network analysis, UCINET (Netdraw) and Pajek. These two programs will be used for larger-scale analysis, using data from the crawling and scraping processes, for which the data will be in different formats. Excel spreadsheets, of course, are not preferred formats for either of these programs, so a bit of conversion had to take place. Luckily, this was not as problematic as trying to get an xml file from an Excel 2007 spreadsheet. Indeed, UCINET itself allows data to be imported from spreadsheets and saved as a matrix that Netdraw will be able to read. The above map, then, is the resulting Netdraw visualisation, using the Spring embedding option in Graph-Theoretic layout. Again, there are options for customising display and layout, and plenty of analytical tools that I haven’t tested yet (going for the visualisation angle first). A bit of refreshing the layout was required, though, to not have the vertices of the island lying on top of each other, thus only having three, rather than five, blogs visible (of course, you can also manually alter the position of vertices).

Pajek visualisation test

From Netdraw, the data could be converted into a Pajek-friendly format, although there is the risk that the layout used by Netdraw can influence that created by Pajek. A bit of playing around and recomputing different layouts negated that, though. Pajek also has the ability to draw the network in 3D, which is a nice option especially when dealing with the implied-three dimensions of the ‘blogosphere’. Similar customisation options to the other programs, although from an aesthetic perspective there’s something rather pleasing about the thin lines and stark colours of the small version of the map. Again, as with UCINET, I’m more likely to use Pajek for larger-scale projects than small maps like this, which I’d probably use a quicker option to go from a spreadsheet to (such as Cytoscape or ManyEyes), but the 3D aspect is handy (especially once I master the export options).

Mage (Netdraw) visualisation test screenshot

Finally, an accidental visualisation. I was testing some of UCINET’s export settings, and ended up somehow revisualising the network in Mage – which, like Pajek can be, uses a 3D layout. I have hardly gone through the options with this visualisation, but after generating all those maps, I was rather taken with the easy ability to rotate the network, including various degrees of shading to further emphasise the position of vertices in the 3D layout. The screenshot doesn’t really do it justice, but again I still need to go through the export options.

All of the tools I tested generated usable maps, with various degrees of customatisation. All except ManyEyes work offline, and all except UCINET (which has a free trial version) are freely available for download (however, .NetMap does require the rather not-free Excel 2007 for most of its stuff, although I think there is a standalone version too…). I imagine there are many other visualisation options available, too, although having more than five or so working options is possibly overkill. Nevertheless, the amount of data and the format used will dictate which visualisation program I use for my work. The ease of going from a basic two-column spreadsheet to the above maps is very pleasing, though, and even with my non-existant background in networks, informatics, stats, and other mathematical abilities, the ability to generate these will help my research.

[I also wanted to test out Gephi, but even after adding extensions to the version of Excel running on here, the xml file exported from Excel with the blog links has not yet been imported successfully by Gephi. Still, it’s another program that I will keep an eye on and try to get working later.]

Written by Tim

3 November, 2008 at 5:09 pm