  We are faced with data comprised of entities interacting over time: this can
be individuals meeting, customers buying products, machines exchanging packets
on the IP network, among others. Capturing the dynamics as well as the
structure of these interactions is of crucial importance for analysis. These
interactions can almost always be labeled with content: group belonging,
reviews of products, abstracts, etc. We model these stream of interactions as
stream graphs, a recent framework to model interactions over time. Formal
Concept Analysis provides a framework for analyzing concepts evolving within a
context. Considering graphs as the context, it has recently been applied to
perform closed pattern mining on social graphs. In this paper, we are
interested in pattern mining in sequences of interactions. After recalling and
extending notions from formal concept analysis on graphs to stream graphs, we
introduce algorithms to enumerate closed patterns on a labeled stream graph,
and introduce a way to select relevant closed patterns. We run experiments on
two real-world datasets of interactions among students and citations between
authors, and show both the feasibility and the relevance of our method.
