  Large-scale interaction networks of human communication are often modeled as
complex graph structures, obscuring temporal patterns within individual
conversations. To facilitate the understanding of such conversational dynamics,
episodes with low or high communication activity as well as breaks in
communication need to be detected to enable the identification of temporal
interaction patterns. Traditional episode detection approaches are highly
dependent on the choice of parameters, such as window-size or
binning-resolution. In this paper, we present a novel technique for the
identification of relevant episodes in bi-directional interaction sequences
from abstract communication networks. We model communication as a continuous
density function, allowing for a more robust segmentation into individual
episodes and estimation of communication volume. Additionally, we define a
tailored feature set to characterize conversational dynamics and enable a
user-steered classification of communication behavior. We apply our technique
to a real-world corpus of email data from a large European research
institution. The results show that our technique allows users to effectively
define, identify, and analyze relevant communication episodes.
