Networks Of Networks

I’m a member of a variety of business and technical networks, each with their own different styles and benefits. Some are quite formal, others very informal. I quite like this overlapping mixture as it often leads to insights that I wouldn’t get any other way.

However, in my experience, many people tend to participate in just one type of network, that most closely aligned to their formal profession (project management, knowledge management etc).

We may be missing some tricks here.

In fact it’s always surprised me how networks aren’t developed and exploited more imaginatively as they play such a key role in innovation and growth. Understanding how they might function differently and better is obviously an important quest.

So, as a step in this direction, I was interested to read a recent research article by Saumell-Mendiola and co that investigates the consequences of linking two complex networks and comes up with some surprising results (slightly edited):

Many real networks are not isolated from each other but form networks of networks, often interrelated in non-trivial ways. Here, we analyze an epidemic spreading process taking place on top of two interconnected complex networks. We develop an approach that allows us to calculate the conditions for the emergence of an endemic (=self-sustaining) state. Interestingly, a global endemic state may arise in the coupled system even though the epidemics are not able to propagate on each network separately, and even when the number of coupling connections is small. Our analytic results are successfully confronted against large-scale numerical simulations.

Endemic state:

In epidemiology, an infection is said to be endemic in a population when that infection is maintained in the population without the need for external inputs. For example, chickenpox is endemic (steady state) in the UK, but malaria is not. Every year, there are a few cases of malaria acquired in the UK, but these do not lead to sustained transmission in the population due to the lack of a suitable vector (mosquitoes).

As noted:

That’s exactly the kind of subtle, emergent behaviour that network scientists have found it hard to understand and model with models of single networks.

Incidentally, this model has important real world applications. There are plenty of examples of coupled networks, such as the spread of sexually transmitted disease in heterosexual and homosexual populations. These populations are largely separate but linked by a few bisexual individuals.

Whilst the above result comes from a specific model and it’s set of assumptions, it’s tempting to speculate how similar behaviour might happen in certain business networks where the transmission of a new idea or approach might spread through just a few contacts in a self-sustaining manner (the idea ‘takes off’ and gets embedded across disciplines).

Network science is a relatively new and fascinating area. If you’d like some references for further reading, take a look at Complex Networks.

In particular, the books by:

are both good introductory reads and the recent book by

has some stunning visualisations of complex networks, covering both the arts and sciences.


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