A network of networks

Fascinating. And visually attractive. The people of LinkedIn Labs just recently created this InMaps application, a kind of analytics tool to “visualize your professional network, clustered in realtime based on their inter-relationships”. A pretty cool tool actually, and I’m a sucker for these kind of applications.

Log in with LinkedIn and the tool will analyze your network and visualize it in a graph like the one below, which is actually the output of my LinkedIn network.

What’s extra interesting about it is that the output is dynamic (unlike this image) and that you can hover over each contact to see their specific connections within your network. That way you also get a view of how the clusters are made and InMaps allows you to put a label on each colored cluster to make it easier to see who’s who. Just give it a try, you’ll see for yourself.

Interesting results for myself is to see for instance that I have 2 Microsoft clusters (I’m ex-Microsoft remember), one for MSN/Windows Live related contacts and one for more general Microsoft contacts. Interesting to see that this split is made, although it’s actually pretty logical when you look at it. Also interesting is to see between which groups exist more links, not always what you would expect. I’m definitely not done analyzing this, but curious what your graph/learnings look like so please do share ;)

Last but not least, it’s also pretty interesting proof that people are organized in groups, clusters and that if you want to influence people it’s important you understand these clusters – or ‘spheres of influence’ like we used to call them, dixit David Armano.

twInfluence

A tweet from Sarah Perez (@sarahintampa) got me curious about a site called twInfluence.com two weeks ago. Anything that has to do with the measurement of online influence interests me, so this was no different. Just like most other services that measure/calculate some form of influence, this one also got it’s own ranking etc figured out, but it was how they got to it that interested me most.

Ever since I’ve started writing about measuring influence, I’ve highlighted there’s too much focus on quantity versus quality. Take the Technorati “authority” for instance, that’s basically just a number of incoming links. Or talking about Twitter and the obsession that many in Twitterville seem to have with the number of followers, or even less relevant: the number of tweets sent out. Never noticed how many times Scoble will refer to the amount of likes he has added on Friendfeed?

It’s not just about quantity at all, but also about the quality of the network, in the case of twitter that translates into the quality of your followers. twInfluence is interesting because it’s the first service that I know that takes exactly that into account. Even if you have only 1 follower, if that is someone with several thousands of followers that is clearly better than the case where you would have a few dozens of followers yourself but all with only few followers themselves:

“First and Second Order Networks: From the perspective of graph theory, a Twitterer’s followers would be considered their first-order network, and their "followers count" the same as their "degree". "Degree" is a simple form of centrality measurement that equates to "prestige" or "popularitiy"; different types of centrality can measure connectivity, authority, and control in a network. The following diagram demonstrates the different "neighborhoods" in a network. The Twitterer is the primary node (shown in red); its first-order neighbors (shown in green) surround it, and its second-order neighbors (shown in blue) surround the outside.”

Another interesting metric twInfluence calculates – what they call efficiency – is the amount of followers you have versus how many people you are following yourself. The site also analyzes velocity (how quickly you’ve gained quality followers) and social capital (how many high-influence people follow you).

twinfluence

Hopefully they’ll also find a way to take out duplications in measuring reach, and there are some opportunities as well to find out about locally relevant influence, but overall this is a very interesting exercise so kudos to the folks behind twInfluence.

When did we start trusting strangers?

“When did we start trusting strangers” is a new research from Universal McCann done in September of this year and is part of their Wave global digital research program. The research/survey was done in 29 countries involving 17.000 internet users.

“It explores how the web and in-particular social media have made it incredibly easy to source and share personal opinions. This has created a revolution in where we source information and what we trust that has massive impacts for the role of professional media and marketing communications.“

I strongly recommend that you take a look at the presentation as it holds some pretty valuable and recent information on consumer behavior and commercial influence. You can find the presentation below, there’s one thing I wanted to highlight specifically though. At a certain point the research talks about superinfluencers:

“In a world of mass influence – some people rise above the average. These are the individuals that influence regardless of category. This is why we call them superinfluencers – they go beyond the average.”

Now that is nothing new, but then they look at these superinfluencers motivations to recommend products or services to their peers (indexed against all respondents) and then you get this:

superinfluencers

You’ll notice that these motivations are pretty similar to all respondents when you look at good or bad personal experiences or when it involves high quality brands, but that they are a lot more driven than the rest of the population by values such as celebrity endorsement/usage, fashionable brands or in case brands are unknown amongst their social group. Now I found that pretty interesting.

Anyway, as said, interesting research and good presentation so go check it out below: