Title: Stochastic Block Model Reveals the Map of Citation Patterns and Their Evolution in Time,
Abstract: In this study we map out the large-scale structure of citation networks of
science journals and follow their evolution in time by using stochastic block
models (SBMs). The SBM fitting procedures are principled methods that can be
used to find hierarchical grouping of journals into blocks that show similar
incoming and outgoing citations patterns. These methods work directly on the
citation network without the need to construct auxiliary networks based on
similarity of nodes. We fit the SBMs to the networks of journals we have
constructed from the data set of around 630 million citations and find a
variety of different types of blocks, such as clusters, bridges, sources, and
sinks. In addition we use a recent generalization of SBMs to determine how much
a manually curated classification of journals into subfields of science is
related to the block structure of the journal network and how this relationship
changes in time. The SBM method tries to find a network of blocks that is the
best high-level representation of the network of journals, and we illustrate
how these block networks (at various levels of resolution) can be used as maps
of science.