Random Etc. Notes to self. Work, play, and the rest.
DARTZ! - St. Petersburg
My younger brother plays guitar in a band called DARTZ! (myspace.com/darts). They recorded an album in London this summer and the first single is out this time next month. There's now a rather swish video for it on youtube*, and local radio recently played it twice.
In other news, a fan of theirs put some of their lyrics on Newcastle's Secret Flickr Wall. If you obsess over Flickr for long enough, eventually Flickr will obsess over you.
* Not embedded because Wordpress is too damn opinionated to let me post invalid HTML dammit.
Flock
Hype alone is usually enough to make me go "meh" and ignore something until it's part of the furniture. Something finally made me look at Flock though, and I'm glad I did.
Flickr integration alone means that if you're already a Firefox/Flickr user it's worth checking out the latest beta. I'm still working out how it interfaces with del.icio.us and multiple blogs, but it's found all my Blogger blogs already. If you're reading this then the blog editor works!
It seemed a little laggy in the interface, but I think that was whilst it was indexing my history in the background - it's better now. We'll see if I'm still with it in a week or two, but for now I'm using it as my primary browser. I even uninstalled Firefox... imagine!
The X of Y
I'm playing with YouTube. I'm a bit confused as to why I have to write my own RSS feeds out for all my friends, but apart from that it's doing most things right and not getting in my way. Fun!
YouTube is the Flickr of video, or something. I discovered today that this lazy turn of phrase is known as a snowclone. Undeterred, I watch with anticipation as Steve takes steps towards making OpenStreetMap into the Flickr of GPS traces. (It's already the wikipedia of maps, of course.)
Flickr Zoom
Flickr's photo notes are great, but often they only work with reference to larger versions of the photo. Since the Flickr API can be called from Javascript, I keep wanting a script that looks for bigger versions of the photo and offers a zoom function for notes (perhaps opening clipped versions of the note in a pop-up window?).
Threadr
So long as Threadless keeps printing great new designs, reprinting old (so-called limited edition) designs and hosting $10 sales, the chances of turning up to work/pub/party wearing the same shirt as your other 'net-savvy friends is only going to increase. Enter Threadr, Threadless vs. Flickr, an online app which powers a wardrobe mounted display, showing you which Threadless T-shirts your peers are wearing today.
Clayton James Cubitt meets Processing, Jim meets Flickr
Clayton James Cubitt is "one of a new breed of photographers no longer content to draw a distinction between the worlds of fashion, art, and porn" apparently. He also has an eye for generative artwork, and my Processing experiments with attractors and particles were enticing enough for him to get in touch and ask for more. A weekend of hacking and a flurry of emails produced user-unfriendly software which would produce images at print resolution, and then I left the rest to Clayton. Witness the fruits of our collaboration in the denim issue of Metropop magazine. I'm really pleased with how it's turned out, and you should definitely check out the rest of his work if you like what you see there.
Staying with photography, (but not at the professional/fashion end of the spectrum) Jim, my most well-travelled of friends, is posting a series of great and varied photos at Flickr, with especially flowery captions. I like! Plus, today he grabbed chocolate as his URL, which must be something of a coup as Flickr hits the mainstream.
Half-baked thoughts on Social Network Visualisation for Flickr
Sites like Flickr (Friendster, Orkut, Tribe, MySpace, etc.) are collecting masses of data about people. Person A knows Person B, B knows C and D, D knows E, who knows A, and so on. If you're into your discrete maths (or your systems thinking), you'll know that the formalisation of these kinds of connections is called Graph Theory. If not, then bear in mind that in graph theory a Graph is composed of Nodes (or points) and Edges (or connections). If A,B,C,D,E are nodes, then "A knows B" is an edge between A and B. (Or if Flickr is a pair of shoes, A,B,C,D,E are the eyelets, and "A knows B" is a shoelace.)
My first example can be drawn in 2D like so:
Plotting these relationships in 2D, or 3D space is known as graph embedding, and finding useful ways to interrogate them is an intriguing problem to think about. Indeed, it's a research field in its own right. Graphs are well understood data structures and many tools are available to manipulate and analyse them (e.g. GraphViz). One reason for this is that they are the cornerstone of many computer science problems (and solutions), but also because they can be used across many different disciplines. The ubiquitous application of graph theoretical methods across many scientific disciplines is the main subject of recent popular science books such as Linked and Ubiquity.
Social Network Visualisation tends to borrow heavily, if not totally, from graph theory. Having looked at several examples of this over the past year, I've spotted some common pitfalls which I'll try and articulate here.
Graph Visualisation Pitfalls
On Flickr, we can easily find out if A knows B by looking to see if A has listed B as a contact. But listing of contacts is tied up with all sorts of other practical considerations. The main reason A adds B as a contact isn't so that we can use that data for social network analysis, unfortunately. On Flickr, it says A knows B, or A likes B's photos, or A chatted to B in the forums and might want to find them again, or B added A (for any of these reasons) so A was being polite and reciprocated, and so on. Not all connections are made equal.
In reality A might have anywhere from 0 to 500 contacts. As the average number of contacts creeps upwards, the naive attempt to draw the graph falls over. My ASCII art attempt would be screwed as soon as there was a group of five mutual acquaintences. Even with the assistance of mature software like GraphViz, the network of X knows Y is too dense to draw clearly. The not-a-tree problem is faced by many network visualisations. There are probably too many connections to graph.
For many of us, it's intuitive to visualise these relationships as a tree. This cluster is connected to that cluster, and people are either in one cluster or another. Clusters probably have sub-clusters. We can handle these kind of relationships easily, but unfortunately they fall down straight away when presented with real data. For example, grouping people by handed-ness is a trivial example of something which generates overlapping sets. Imagine A and B are left-handed, D and E are right-handed, but C is ambidextrous. We aren't dealing with heirarchical clusters, we're dealing with overlapping sets.
A knows B, B knows C, C knows D. What's the connection between A and D? If we're analysing a terrorist network, then we might have found a potential link which is worth investigating further. But if we're trying to recommend photography or music or web-links to A, should we include D's tastes? If D is C's drug dealer, and B is C's little sister, and A is C's elderly next door neighbour? Probably not. Connections aren't transitive.
Show Me The Eye Candy Anyway
So assume we're aware of the above caveats, and we have a densely but ambiguously connected graph of contacts. The main task with this kind of visualisation, once you have the data, is how to display it in a readable format. It's almost certainly too much data to just throw out there (but it's always worth a try), so how do we prune it down to show only the meaningful stuff?
On Flickr, GustavoG has been busy producing graphs of the mutual contacts and testimonials networks. You can see them all in his FlickrLand set. These are interesting to examine for many reasons, not least in how he has pruned the network in order to get manageable visualisations from it. The whole social network on Flickr would be too big to show in detail, so Gustavo doesn't show it all. He rightly spots that testimonials should indicate fairly strong ties, and the network is much sparser than the contacts network. He's also attempts to trim the constacts network down by set the requirement that contacts must be mutual for a graph edge to exist, and he's tried different thresholds for how many mutual contacts a person must have before they are added to the graph.
Gustavo has used yEd, a Java graph layout program, to produce graphs using the "organic layout", and it works pretty well. In particular, in certain graphs there are undeniably meaningful clusters to be found, ones that expert users can spot straight away. At 50 mutual contacts and 100 mutual contacts in particular, the clusters are pronounced. At 10 mutual contacts the network is too dense to be very meaningful - certainly as it's presented at the moment it doesn't say much at all. At 200 mutual contacts, it tells us what many regulars to Flickr already know - there are only a few very well connected folks, and they mostly know each other. Because testimonials indicate stronger ties, the overall network is very fragmented, leaving many loose mini-clusters. Nevertheless, the overall picture is surprisingly well knitted together.
FlickrGraph
Gustavo's method for making the data manageable is to remove nodes which aren't significant in the overall picture. For instance, in mapping out clusters of users on Flickr, it's probably not a big deal to lose people with less than 10 mutual contacts. Marcos Weskamp recently used a different tactic to cut down the same data - remove edges, and only show a subset (window) of nodes at any one time.
Marcos's FlickrGraph is fantastic to play with - the interactivity and clean design help there - but unfortunately it is less meaningful than Gustavo's static graphs.
The FlickGraph suffers from the data source because the current Flickr API will always return contacts sorted by user-id (an essentially arbitrary number). Because of this, and the limit of ten contacts shown, a lot of popular people won't appear to be connected to anyone despite the fact that they are.
Ways To Improve These Visualisations
Wanted: Richer Data.
We already know how to get meaningful visualisations from our data. We have to start with meaningful data. Marcos Weskamp has demonstrated a neat way of graphing mailing list interactions with his Social Circles project. In my opinion, the FlickrGraph lacks some of the insight that Social Circles provides. This is partly due to technical implementation, but mainly because Social Circles is sourced from real interactions and implicit connections, not watered-down explicit declarations of interest. Flickr users like HyperBob are very active in the forums and comments, but don't keep contacts on Flickr. Social Circles-style data would capture that.
On Flickr, there are several implicit contacts networks we could use:
- Comments. A comments on B's photo. A-B could be an edge. A and B hold a conversation about the photo by comments. B-A should be an edge too.
- Favourites. A marks B's photo as a favourite. A-B is an edge. The edge length can vary depending on how many favourites A has, how many photos B has, how many of B's photos are marked as favourites by A, etc.
- Forum posts. A and B participate in the same discussion. A-B is an edge. The edge length could vary with the number of times they post in a single discussion, or the number of shared discussions they have.
- FlickrMail. As with forums, but this data is totally private, so I have no idea what it would look like or how to get it.
- FlickrLive. Who chats with whom?
- Page views. If A views B's photos regularly, but doesn't mark B's photos as favourites, and never comments on B's photos. (The privacy implications for this one are numerous - not least the fact that there is a reasonable amount of risque content on Flickr which people might admire but deliberately not declare an interest in, especially in an explicit way through contacts/favourites/comments or otherwise.)
The idea here is that we should be visualising the actual social things people are doing, rather than the social acts they say they are doing. I also touched upon these ideas in a post about Architects, Social Networks and Hypertext. But enough of my ranting, I'm off to try one of these methods.
Flickr Favourites
Doing my bit for the discovery of interesting stuff, I present: a slideshow of my Flickr favourites.
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