Biological, human, and organizational realities are networked. Complex environments are networks. Computers are networked. Epidemics are networks. Business relations are networked. Thought and reasoning are in neural networks. Emotions are networked. Families are networks. Politics are networked. Culture and social relations are, too. Your network is your net worth.
Yet the general public does not yet "speak networks."
Network concepts are still new to many, and not widely enough spread. I have in mind structural traits like peer-to-peer and packet switching, process qualities such as assortativity, directionality and reciprocity, indicators such as in- and out-degree, density, centrality, betweenness, multiplexity and reciprocity, and ideas such as bridging vs. bonding, Simmelian cliques, network effects and strength of ties. These amount to a language and analytical approach whose time has come.
Much of scientific thought in the last century, especially in the social and life sciences, has been organized around notions of central tendency and variance. These statistical lenses magnified and clarified much of the world beyond earlier, pre-positivist and less evidence-based approaches. However, these same terms miss and mask the network. It is now time to open minds to an understanding of the somewhat more complex truths of networked existence. We need to see more networks in public coverage of science, more in media reporting, more in writing and rhetoric, even more in the teaching of expression and composition. "Network speaking" beckons more post-linear language.
Some of the best minds of the early 21st century are working on developing a language that is not yet known, integrated or spoken outside their own small circle, or network. It is this language of metaphors and analytical lenses, which focuses on networks, that I propose be shared more widely now that we are beginning to see its universal value in describing, predicting, and even prescribing reality.
In an era of fascination with big data it is too easy to be dazzled by the entities ("vertices") and their counts and measures, at the expense of the links ("edges"). Network ideas bring the connections back to the fore. Even if these are hidden from or in plain view they are the essence. Statistics, and especially variance-based measures such as standard deviation and correlation analyses are reductionist. Network lenses allow and even encourage a much needed pulling back to see the broader picture. In all fields, we need more topology and metrics that recognize the mesh of connections beyond the traits of the components.
As with literacy, recent generations witnessed an enormous leap in numeracy. More people know numbers, are comfortable with calculations, and can see the relevance of arithmetic and even higher math to their daily lives. Easy access to calculators, followed by widespread access to computation devices have accelerated the public's familiarity and comfort with numbers as a way of capturing reality, predicting and dealing with it.
We do not yet have the network equivalent of the pocket calculator. Let's make that a next improvement in public awareness of science. While a few decades ago it was clear the public needed to be taught about mean, media and mode, standard deviations and variance, percentages and significance, it is now the turn of network concepts to come to the fore. For us to understand the spread of truth and lies, political stances and viruses, wealth and social compassion, we need to internalize the mechanisms and measures of the networks along which such dynamics take place. The opposite of networking is not working.