A network diagram tends to represent every node the same visual size, as though position in the network were the only variable that mattered, when in practice some nodes carry far more relational weight than their diagram size suggests — hubs that, if removed, would fragment the network, versus peripheral nodes whose removal would barely register. Nodal density names this weight directly: what makes a node significant is not how large it happens to be drawn but how densely it sits within the relations connecting it to everything else. InsightViscosity names a related but distinct property — how slowly or quickly an insight generated at one dense node actually propagates outward through the surrounding network, since high density does not guarantee fast transmission and can sometimes produce the opposite, a viscous pooling of insight that struggles to reach less-connected regions of the same network.
Beer, S. (1972) Brain of the Firm: The Managerial Cybernetics of Organization. London: Allen Lane.
Bertalanffy, L. von (1968) General System Theory: Foundations, Development, Applications. New York: George Braziller.
Barabási, A.-L. (2016) Network Science. Cambridge: Cambridge University Press.
DeLanda, M. (2006) A New Philosophy of Society: Assemblage Theory and Social Complexity. London: Continuum.
Castells, M. (1996) The Rise of the Network Society. Oxford: Blackwell.