Circles vs Community Detection

One take on the story so far:

– Facebook supports symmetrical follows and allows you to see connections between your Facebook friends;
– Twitter supports asymmetric follows and allows you to see pretty much everyones’ friend and follower connections;
– Google+ supports asymmetric follows

Facebook and Twitter both support lists but hardly anyone uses them. Google+ encourages you to put people into addressable circles (i.e. lists).

If you can grab a copy of connections between folk in your social network, you can run social network statistics that will partition out different social groupings:

My annotated twitter follower network

If you’re familiar with the interests of people in a particular cluster, you can label them (there are also ways you might try to do this automagically).

Now a Facebook app, Super Friends, will help you identify – and label – clusters in your Facebook network (via ReadWriteWeb):

Super friends facebook app

This is a great feature, and something I could imagine being supported to some extent in Gephi, for example, by allowing the user to create a node attribute where the values represent label mappings from different modularity clusters (or more simply by allowing a user to add a label to each modularity class?).

The SuperFriends app also stands in contrast to the Google+ approach. I’d class SuperFriends as gardening, whereas he Google+ approach is more one of planning. The Google+ approach encourages you to think you’re in control of different parts of your network and makes your life really complicated (which circle do I put this person in; do I need a new circle for this?); the SuperFriends approach helps you realise how complicated (or not) your social circle is. In terms of filters, the Google+ approach encourages you to add your own, whereas the SuperFriends approach helps you identify setting that emerges out of network properties.

Given that in many respects Google is an AI/machine learning company, it’s odd that they’re getting the user to define circle/set membership; maybe it’d be too creepy if they automatically suggested groups? Maybe there’s too much scope for error if you don’t deliberately place people into a group yourself (and instead trust an algorithm to do it?)

Superfriends helps uncover structure, Google+ forces you to make all sorts of choices and decisions every time you “follow” another person. Google+ makes you define tags and categories to label people up front; SuperFriends identifies clusters that might be covered by an obvious tag.

Looking at my delicous bookmarks, I have almost as many tags a bookmarks… But if I ran some sort of grouping analysis, (not sure what?!) maybe natural clusters – and natural tags – would emerge as a result?

Maybe I need to read Everything is Miscellaneous again…?

PS if you want to run a more hands on analysis of your Facebook network, try this: Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part I

PPS here’s another Facebook app that identifies clusters: h/t @jacomyal

PPPS @danmcquillan also tweeted that LinkedIn InMaps do a similar clustering job on LinkedIn connections. They do indeed; and they use Gephi. I wonder if they’ve released the code that handles things from the point at which a social network graph data is prpvided to the rendering of the map?

Author: Tony Hirst

I'm a Senior Lecturer at The Open University, with an interest in #opendata policy and practice, as well as general web tinkering...

4 thoughts on “Circles vs Community Detection”

  1. I was thinking along these lines recently as well. I’m all for automatically suggested groups which can be edited and tweaked based on the need and the network. Especially after building up a social graph in so many different social networks. Social graph should be an open standard for application in any network. Blogged about it here:

    1. @michael Being able to organically grow a network and then crystallise meaningful groupings based on things like network analysis certainly appeals to me… I’ve also started exploring ways of using curated lists + social network analysis as a means of discovering communities (eg by harvesting members of lists in a topic area and then plotting the social network connections amongst those people).

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