Complex physical and social systems find a handy representation in terms of graphs, which, in this context, are called complex networks. Entities in these systems naturally “communicate”, or exchange “information”, e.g., a group of people interacting via email or sharing links, liking posts, and following each other on social platforms, exchange information as part of their social life. Neurons, connected by synapses and fibre bundles, exchange of neuro-physiological signals, enabling cognition. In fish schools, aggregations of fish, who come together in an interactive, social way, the (possibly passive) communication between fish allows them to act as a super-system. All complex systems show some emergent behaviour that cannot be ascribed to the actions and behaviour of their individual components. This emergent behaviour is a function of both the interaction patterns, i.e. the links in the graph, and the communication strategy, which can be modelled as a dynamical process on the network. In this talk, we will see, firstly, how Markovian random walks on networks model diffusion dynamics in the complex system and why this approach is useful in network science. Then, we will see an example of non-Markovian random walk, which mimics the run-and-tumble motion of bacteria. Eventually, it should become clear how this led me here, trying to learn information geometry.
