  We analyzed agent behavior in complex networks: Barab\'asi-Albert (BA),
Erdos-R\'enyi (ER), and Watts-Strogatz (WS) models under the following rules:
agents (a) randomly select a destination among adjacent nodes; (b) exclude the
most congested adjacent node as a potential destination and randomly select a
destination among the remaining nodes; or (c) select the sparsest adjacent node
as a destination. We focused on small complex networks with node degrees
ranging from zero to a maximum of approximately 20 to study agent behavior in
traffic and transportation networks. We measured the hunting rate, that is, the
rate of change of agent amounts in each node per unit of time, and the
imbalance of agent distribution among nodes. Our simulation study reveals that
the topological structure of a network precisely determines agent distribution
when agents perform full random walks; however, their destination selections
alter the agent distribution. Notably, rule (c) makes hunting and imbalance
rates significantly high compared with random walk cases (a) and (b),
irrespective of network types, when the network has a high degree and high
activity rate. Compared with the full random walk in (a), (b) increases the
hunting rate while decreasing the imbalance rate when activity is low; however,
both increase when activity is high. These characteristics exhibit slight
periodic undulations over time. Furthermore, our analysis shows that in the BA,
ER, and WS network models, the hunting rate decreases and the imbalance rate
increases when the system disconnects randomly selected nodes in simulations
where agents follow rules (a)-(c) and the network has the ability to disconnect
nodes within a certain time of all time steps. Our findings can be applied to
various applications related to agent dynamics in complex networks.
