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 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. @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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