  Swarm intelligence is the collective behavior emerging in systems with
locally interacting components. Because of their self-organization
capabilities, swarm-based systems show essential properties for handling
real-world problems such as robustness, scalability, and flexibility. Yet, we
do not know why swarm-based algorithms work well and neither we can compare the
different approaches in the literature. The lack of a common framework capable
of characterizing these several swarm-based algorithms, transcending their
particularities, has led to a stream of publications inspired by different
aspects of nature without a systematic comparison over existing approaches.
Here, we address this gap by introducing a network-based framework---the
interaction network---to examine computational swarm-based systems via the
optics of the social dynamics of such interaction network; a clear example of
network science being applied to bring further clarity to a complicated field
within artificial intelligence. We discuss the social interactions of four
well-known swarm-based algorithms and provide an in-depth case study of the
Particle Swarm Optimization. The interaction network enables researchers to
study swarm algorithms as systems, removing the algorithm particularities from
the analyses while focusing on the structure of the social interactions.
