“…One of the great mysteries of large distributed systems – from communities and organisations to brains and ecosystems – is how globally coherent activity can emerge in the absence of centralized authority or control… in many systems, usually those that have developed or evolved naturally, the source of control is far from clear.

Nevertheless, the intuitive appeal is such a strong one that network analysts have focused heavily on devising centrality measures… implicit in this approach is the assumption that networks that appear decentralised are really not at all… if we look carefully at the network data, it claims, even a large and complex network will reveal itself to hinge on some small subset of influential players, information brokers and critical resources, which together form the functional center on which everyone else depends. These key players may not be obvious – they might seem to be unimportant by conventional measures of status and power – yet they are always there.

And once they are identified, we are back on familiar terrain, dealing with a system that has a center.  Notions of centrality have been enormously popular in the networks literature… [they don’t] force us to stomach any truly difficult or counterintuitive notions…  The world always has a centre, information is processed and distributed by the centre, and central players wield more influence that peripheral players…

But what if there isn’t a center? Or what if there are many “centers” that are not necessarily coordinated or even on the same side? What if important innovations originate not in the core of a network but in its peripheries, where the chief information brokers are too busy to watch? What if small events percolate through obscure places by happenstance and random encounters, triggering a multitude of individual decisions, each made in the absence of any grand plan, yet aggregating somehow into a momentous event unanticipated by anyone, including the actors themselves?”

(Duncan J Watts)


Join the conversation! 2 Comments

  1. I was surprised to read that Duncan Watts thinks that underlying the development of centrality measures is “the assumption that networks that appear decentralised are really not at all…”

    Many different types of centrality measures have been developed over the years. UCINET network analysis software will analyse about a dozen different types of centrality in network data. Most of these are measures of the centrality of individual actors in a given network, not measures of the structure of the network as a whole. In any network, including randomly constructed networks, there will always be some actors who are more central than others. And those differences will have consequences.

    The internet is a classic emergent network structure. Yet, I think Google’s Page Rank algorithm, which is the basis of its searching facility, uses a variant of a centrality measure known as Eigenvector centrality. This “second hand measure of centrality” assumes that “having links to many other pages is less important than having links to many other pages which themselves have many links to other pages”.

    Re Watt’s other comment, “What if important innovations originate not in the core of a network but in its peripheries, where the chief information brokers are too busy to watch?” Many would argue that focusing on core versus periphery is a false choice. I recommend Ronald Burt’s “Brokerage and Closure: An introduction to social capital”. In his view, and that of many others in the same field, the ability to broker new connections between disparate networks is as important as the ability to capitalise on that brokerage by having access to densely interconnected parts within those networks. Innovation not only involves discovery but also use and adoption by others.

    The whole dichotomy of centre and periphery is a bit of an artifact of the way we see networks. By necessity we are always only viewing part of a much wider network structure. That applies when looking at human societies and when looking at wider biological systems, and probably further afield still! By the way, appreciation of the complexity of networks goes back a long way in history and philosophy. My favorite example is “Indra’s Net” Read the Wikipedia entry here: http://en.wikipedia.org/wiki/Indra%27s_net

  2. on a related topic, you might be interested in the presentations from the recent Harvard workshop on social influence in political networks – they shed some light on the question of centrality (or lack thereof) http://www.iq.harvard.edu/blog/netgov. The Web Ecology project has also done some fascinating analysis of twittering during the Iranian election – and emerging patterns during that period.




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About Ben Ramalingam

I am a researcher and writer specialising on international development and humanitarian issues. I am currently working on a number of consulting and advisory assignments for international agencies. I am also writing a book on complexity sciences and international aid which will be published by Oxford University Press. I hold Senior Research Associate and Visiting Fellow positions at the Institute of Development Studies, the Overseas Development Institute, and the London School of Economics.


Knowledge and learning, Networks