A network is a collection of nodes and edges, where an edge connects two nodes. Many
social, natural and engineered systems can be represented as networks. Examples include …

Recently, social phenomena have received a lot of attention not only from social scientists,
but also from physicists, mathematicians and computer scientists, in the emerging …

This book discusses the study and analysis of the physical aspects of social systems and
models, inspired by the analogy with familiar models of physical systems and possible …

Opinions are an integral part of how we perceive the world and each other. They shape
collective action, playing a role in democratic processes, the evolution of norms, and cultural …

In this introductory chapter, we start by briefly summarising temporal and adaptive networks,
and epidemic process models frequently used in this volume. Then, we introduce a couple …

Interactions among units in complex systems occur in a specific sequential order, thus
affecting the flow of information, the propagation of diseases, and general dynamical …

The complex nature of organizational culture challenges our ability to infer its underlying
dynamics from observational studies. Recent computational studies have adopted a …

The Gillespie algorithm provides statistically exact methods for simulating stochastic
dynamics modeled as interacting sequences of discrete events including systems of …

The traditional way of studying temporal networks is to aggregate the dynamics of the edges
to create a static weighted network. This implicitly assumes that the edges are governed by …

We review the agent-based models (ABM) on social physics including econophysics. The
ABM consists of agent, system space, and external environment. The agent is autonomous …