Temporal networks - Dynamic Networks

Temporal networks are a specific type of network where interactions or connections between nodes have temporal attributes associated with them. In temporal networks, the timing and duration of interactions are considered, providing a more detailed and time-sensitive representation of networked systems.

In a temporal network, edges or links between nodes are not static, but have associated time stamps or durations. This means that the connections between nodes can change over time, and the temporal aspects of interactions are explicitly accounted for. For example, in a social network, a temporal network could capture the sequence of interactions between individuals, including the time when each interaction occurred and its duration

Temporal networks are useful for studying a wide range of dynamic processes. They enable researchers to analyze and model how information spreads, how diseases propagate, how social relationships form and evolve, and how events or behaviors emerge and unfold over time. By incorporating the temporal dimension, temporal networks provide a more realistic and accurate representation of real-world systems, where interactions are not constant but occur in a time-dependent manner.

Analyzing temporal networks involves examining various temporal properties, such as the temporal ordering of events, the intervals between interactions, the burstiness or regularity of activity, and the influence of temporal patterns on network structure and dynamics. Researchers use mathematical models, network analysis techniques, and data mining approaches to investigate temporal networks and uncover patterns, dynamics, and underlying mechanisms of temporal processes in networked systems.

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