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In this study we map out the large-scale structure of citation networks ofscience journals and follow their evolution in time by using stochastic blockmodels (SBMs). The SBM fitting procedures are principled methods that can beused to find hierarchical grouping of journals into blocks that show similarincoming and outgoing citations patterns. These methods work directly on thecitation network without the need to construct auxiliary networks based onsimilarity of nodes. We fit the SBMs to the networks of journals we haveconstructed from the data set of around 630 million citations and find avariety of different types of blocks, such as clusters, bridges, sources, andsinks. In addition we use a recent generalization of SBMs to determine how mucha manually curated classification of journals into subfields of science isrelated to the block structure of the journal network and how this relationshipchanges in time. The SBM method tries to find a network of blocks that is thebest high-level representation of the network of journals, and we illustratehow these block networks (at various levels of resolution) can be used as mapsof science.

Stochastic Block Model Reveals the Map of Citation Patterns and Their Evolution in Time