  This paper presents a contribution to the study of bibliographic corpora in
the context of science mapping. Starting from a graph representation of
documents and their textual dimension, we observe that stochastic block models
(SBMs) can provide a simultaneous clustering of documents and words that we
call a domain-topic model. Previous work by (Gerlach et al., 2018) investigated
the resulting topics, or word clusters, while ours focuses on the study of the
document clusters, which we call domains. To enable the synthetic description
and interactive navigation of domains, we introduce measures and interfaces
relating both types of clusters, which reflect the structure of the graph and
the model. We then present a procedure that, starting from the document
clusters, extends the block model to also cluster arbitrary metadata attributes
of the documents. We call this procedure a domain-chained model, and our
previous measures and interfaces can be directly transposed to read the
metadata clusters. We provide an example application to a corpus that is
relevant to current STS research, and an interesting case for our approach: the
1995-2017 collection of abstracts presented at ASCO, the main annual oncology
research conference. Through a sequence of domain-topic and domain-chained
models, we identify and describe a particular group of domains in ASCO that
have notably grown through the last decades, and which we relate to the
establishment of "oncopolicy" as a major concern in oncology.
