A network with two types of nodes is called 2-mode, bimodal, or bipartite. We can add more, making it multimodal. Publishers, topics, you-name-it. We can even add seemingly unrelated node-types, like academic conferences, or colors of the rainbow. The list goes on. We would have a new list for each new variety of node. Presumably we could continue adding nodes and node-types until we run out of stuff in the universe. This would be a bad idea, and not just because it would take more time, energy, and hard-drives than could ever possibly exist. As it stands now, network science is ill-equipped to deal with multimodal networks. 2-mode networks are difficult enough to work with, but once you get to three or more varieties of nodes, most algorithms used in network analysis simply do not work. It’s not that they can’t work; it’s just that most algorithms were only created to deal with networks with one variety of node.

The newcomer to network analysis loads the author-publisher network into Gephi, finds the centrality of every node, and then makes the nodes bigger that have the highest centrality. The issue here is that, although the network loads into Gephi perfectly fine, and although the centrality algorithm runs smoothly, the resulting numbers do not mean what they usually mean. Centrality, as it exists in Gephi, was fine-tuned to be used with single mode networks, whereas the author-publisher network is bimodal. Centrality measures have been made for bimodal networks, but those algorithms are not included with Gephi.

This reduction of data comes at a price, one that some argue traditionally divided the humanities and social sciences. If humanists care more about the differences than the regularities, more about what makes an object unique rather than what makes it similar, that is the very information they are likely to lose by defining their objects as nodes.

Anyone with a lot of time and a vicious interest in networks should stop reading right now, and instead pick up copies of Networks, Crowds, and Markets (Easley & Kleinberg, 2010) and Networks: An Introduction (Newman, 2010). The first is a non-mathy introduction to most of the concepts of network analysis, and the second is a more in depth (and formula-laden) exploration of those concepts. They’re phenomenal, essential, and worth every penny.

The system of pay-to-subscribe journals that spent so many centuries helping the scholarly landscape coordinate and collaborate is now obsolete; a vestigial organ in the body of science. These days, most universities offer free web access and web hosting. These two elements are necessary, though not sufficient, for a free knowledge economy. We also need peer review (or some other, better form of quality control), improved reputation management (citations++), and some assurance that data/information will last. These come at a cost, but those costs can be paid by the entire scholarly market, and the fruits enjoyed within and without. If you think open access is important, you should also consider pledging to support open access. Publishing companies have a lot of money invested in keeping things as they are, and only a concerted effort on behalf of the scholars feeding and using the system will be able to change it.