Networks also have components, basically independent sub-networks in the network with higher connectivity among the nodes. The largest component of many interlinking nodes – often called the ‘giant component’ – is often surrounded by smaller ‘isolated’ components of interlinking nodes. In general, it holds that beyond a certain base level of connectivity (when each node has on average more than one link) the higher the connectivity of the nodes becomes, the faster and more exponential the growth of the diffusion will be. The chances of the isolated components becoming connected to the centrally located ‘giant component’ increase. The size of the largest component continues to grow exponentially with previously isolated smaller component linking and thus the number of nodes that can be reached efficiently as well. This effect has important consequences for innovation leadership. One innovation leadership tactic for successful diffusion is providing network members in smaller isolated sub-networks surrounding the largest sub-network with platform, tools and motivation, to become more connected – odds are they will connect to the largest component. This is discussed in the frames 4, 5, 6 below. The overall effect is a highly non-linear and steep growth curve of messages or ideas spreading.
Another tactic as explained in frame 2 below is carefully identifying to whom the message or idea is seeded. If innovation leadership can spot the “right” people to start its launching campaign, the diffusion should go through much more easily. The diffusion will be larger if the seeds are hubs, meaning that they have many links – the super-connectors. Also, the more central the hub in the networks is, and the shorter the average path from node-to-node (popularized as “degrees-of-separation”), the easier diffusion will take place. A limited number of nodes (people) with many links can work as “bridges” between regions of nodes with lower connectivity. The example of transportation networks is meaningful: large airports, the aviation hubs, a few years ago were perceived as the fundamental means of transmission of the SARS epidemic.
The final notion to consider for diffusion in a network is clustering. For the adoption of a new product, as long as the adoption decision depends not just on personal preferences and evaluation, but also on a kind of “social” or “peer” effect, high clustering in a network makes diffusion easier. A consumer adopts with a probability that increases with the number of neighbors adopting. This notion is behind frames 1 and 3.
The seven scaling frames of Networks presented in this article provide innovation leadership with practical perspectives on how new products can spread rapidly leveraging the properties of networks.