Social ‘networks’, computer ‘networks’, ‘networking’ events, agency ‘networks’ – even the ‘Internet’. ‘Networks’ of one form or another are everywhere, but what do all these things have in common? And how can an understanding of networks help us become better digital marketers?

I want to go beyond our often unthinking use of the word network, and explore how a deeper understanding of networks can help us identify influencers and communities online, creating content that really resonates and is more likely to be shared.

I’ll use an example of a network generated from Twitter, showing the follow relationships between people talking about Econsultancy, and sharing links from the site, over an eight day period in October 2013.

A network map of 3,930 users sharing content on Twitter
A network map of 3,930 Twitter users talking about and sharing Econsultancy links over an eight day period in October 2013

What is a 'network'?

At its most basic, a network can be defined as a collection of ‘nodes’ (things like people, products, or web pages) connected by relationships or associations of some kind (known as ‘edges’).

Mathematicians have been wrestling with networks for hundreds of years (through 'graph theory'), and more recently economists, sociologists and computer scientists have applied the mathematicians' insights to solve real-world problems.

There are many ways that understanding networks can help us become better digital marketers. A network algorithm, PageRank, underlies Google’s ordering of search results and allowed the company to power ahead of less network-savvy search engines such as Lycos, AltaVista and Yahoo! in the late 90s. 

Networks are also central to the way that Facebook allows users to see and now find content, for example through Graph Search.

Network analysis also helps us understand patterns of social interaction between users on blogs, forums, and social media sites.

The Econsultancy network

In our example Econsultancy Twitter network we queried the Twitter API to find the users who mentioned, or shared a link from, Econsultancy over an 8-day period, and then worked out the relationships between them. 

Detail of a network map of 3,930 users sharing content on Twitter
A detail of the network map of 3,930 users sharing content on Twitter

An increasing range of tools – including BlueNod, BottleNose, and various social media listening tools – allow us to gather Twitter network data. There’s also a range of free and paid software (NodeXL, yEd, Gephi, Mathematica) that allows us to analyse and visualise this data once we have it.

The ‘edges’ in our Econsultancy network are follower relationships between the users. Twitter networks made of other kinds of edges are possible. For example, we could decide to create an edge when one user mentions another, or retweets something they have tweeted. 

But a network of follow relationships contains the richest and most complete data – because users follow so many more users than they interact directly with, and a ‘follow’ is a vote of confidence – or at least an expression of interest – in the user who is followed.

It shows that someone cares about their opinion and wants to hear it. It also gives us a clue about the direction in which information flows through this group of people, from the followed to the follower.

The Econsultancy 'community of interest'

Just by looking at the Econsultancy network we can see something fairly remarkable.

The vast majority of users (99.26% to be exact, or 3,901 of 3,930) are bound together in a single group – or network ‘component’, to use the technical term.

Every user in this group is connected, either directly or indirectly through others, to every other user. Why should this large group of people who seemingly share nothing apart from having mentioned Econsultancy on Twitter be bound together like this? 

There are a few arcane mathematical reasons that make it more likely, but the fundamental reason is that the people in the network are a ‘community of interest’, defined by a shared enthusiasm for digital marketing in general, and in particular.

Real-life communities tend to be bound together by geography (a school, a workplace, a city), but online communities of interest are much more frictionless.

The connectedness of all these people talking about Econsultancy is pretty remarkable, when you think about it, and should give us a clue that something important is going on just below the surface.

In future posts I’ll be showing how we can look at the Econsultancy network to work out who is most influential, and to identify communities and cliques. I’ll also show how a Twitter network model can help us tailor content so that it really resonates, and is disseminated as deep into the network as possible.

Andrew Lamb

Published 29 October, 2013 by Andrew Lamb

Andrew Lamb is Senior Digital Strategist - Healthcare at Ketchum / Inspired Science and a contributor to Econsultancy. You can connect via Twitter or  Google Plus

3 more posts from this author

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Comments (7)

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Hugh Anderson, Co-Founder & Director at Forth Metrics Limited

You've got me interested, Andrew. "Social network analysis" is an area that we are exploring and we agree that there is significant potential in it. The challenge is translating the science into commercial value, which is what is missing from this post, but I assume this was just the intro, so I look forward to the future posts :)

almost 5 years ago

Andrew Smith

Andrew Smith, Director at eschermanSmall Business

Good stuff. You may be interested in my chapter on Network Topology in the CIPR's Share This Too book, published by Wiley last month ;)

almost 5 years ago


Olga Militsi, Marketing Manager at 2KM LTD

Looking forward to your future posts Andrew. I am currently taking a course on Social Network Analysis and familiarizing myself with tools like Gephi. The commercial value, as Hugh has mentioned, is what we are interested in at the end of the day. The ability to target efficiently with more relevant content and better circulation of information is how I have interpreted it so far.

almost 5 years ago

dan barker

dan barker, E-Business Consultant at Dan Barker

hi, Andrew, this is wonderful! I'm looking forward to more in the series.

One tiny note on this:

"Every user in this group is connected, either directly or indirectly through others, to every other user. Why should this large group of people who seemingly share nothing apart from having mentioned Econsultancy on Twitter be bound together like this?"

Is it not possible that some of this is causal, rather than just a coincidence? eg. If the overall network is defined as people who have mentioned Econsultancy, I'd presume that many of them also follow Econsultancy (which may even be the reason they're mentioning the brand - eg, manually retweeting). And, if they're connected to Econsultancy, they would at least be indirectly linked.

Anyway - I really enjoyed reading this & would love to meet up for a coffee in London if you're ever around.


almost 5 years ago

Andrew Lamb

Andrew Lamb, Senior Digital Strategist – Healthcare at the Unlimited Group

Andrew – I'm aware of and really enjoyed your chapter in Share This Too!

Dan – glad you enjoyed the piece. That's exactly it, everyone in the network shares an interest in Econsultancy (and probably also the @Econsultancy account) so it is more than just coincidence. SNA is great at making these real-life 'communities of interest' visible.

almost 5 years ago

Andrew Lamb

Andrew Lamb, Senior Digital Strategist – Healthcare at the Unlimited Group

Hugh and Olga – hopefully future posts will address the commercial value in more detail. Part two, on finding influencers, is live now. Another one on identifying communities and optimising content is also on its way!

almost 5 years ago


Prem Sankar

You can analyse the adoption possibilities of followers in this network .When you pass an advertisement or a message to followers ,which of the followers will adopt the message and how this message can spread as 'viral' in twitter

almost 5 years ago

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