  Information on social media spreads through an underlying diffusion network
that connects people of common interests and opinions. This diffusion network
often comprises multiple layers, each capturing the spreading dynamics of a
certain type of information characterized by, for example, topic, attitude, or
language. Researchers have previously proposed methods to infer these
underlying multilayer diffusion networks from observed spreading patterns, but
little is known about how well these methods perform across the range of
realistic spreading data. In this paper, we first introduce an effective
implementation of the inference method that can achieve higher accuracy than
existing implementations in comparable runtime. Then, we conduct an extensive
series of synthetic data experiments to systematically analyze the performance
of the method, under varied network structure (e.g. density, number of layers)
and information diffusion settings (e.g. cascade size, layer mixing) that are
designed to mimic real-world spreading on social media. Our findings include
that the inference accuracy varies extremely with network density, and that the
method fails to decompose the diffusion network correctly when most cascades in
the data reach a limited audience. In demonstrating the conditions under which
the inference accuracy is extremely low, our paper highlights the need to
carefully evaluate the applicability of the method before running the inference
on real data. Practically, our results serve as a reference for this
evaluation, and our publicly available implementation supports further testing
under personalized settings.
