Description: Temporal Networks are structures that represent relationships and connections that evolve over time. Unlike static networks, where relationships are fixed and do not change, temporal networks allow for the dynamic modeling of how interactions between nodes (which can be people, organizations, devices, etc.) modify over time. This feature is crucial for understanding complex phenomena across various disciplines, as many real-world interactions are inherently temporal. Temporal networks consist of nodes and edges, where edges can have temporal attributes indicating when a relationship was established, modified, or broken. This allows researchers and analysts to study behavioral patterns, information diffusion, and relationship evolution in contexts such as social networks, biology, and communication systems. The ability to capture the dynamics of relationships over time provides a richer and more accurate view of how complex systems operate, facilitating the identification of trends and the prediction of future behaviors.
History: Temporal Networks began to gain attention in the 2000s when researchers started to realize that many interactions in social networks and complex systems were not static. In 2005, the work of researchers like Petter Holme and Jari Saramäki helped formalize the concept and establish methods for analyzing these networks. Since then, the field has rapidly evolved, driven by the increase in available temporal data and the development of new analytical techniques.
Uses: Temporal Networks are used in various fields, including sociology to study the evolution of social relationships, in epidemiology to model the spread of diseases, and in communication systems to analyze how information flows through networks. They are also useful in transportation network analysis and in biological systems research, where interactions change over time.
Examples: An example of a Temporal Network is the analysis of interactions in social networks like Twitter, where users can follow and unfollow others at different times. Another case is the study of the spread of infectious diseases, where relationships between individuals can change as new infections and recoveries occur.
