Evolving networks

Evolving networks are networks whose nodes, edges, or both change over time. Their study examines how these changes shape connectivity, growth, and adaptation.

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Preferential attachment: A growth mechanism in which new nodes connect more often to nodes that already have many links. It explains how repeated edge additions can generate highly connected hubs.
Graph theory: The mathematical study of graphs, structures made of vertices connected by edges. It supplies the basic language for describing network nodes and links.
Social network: A network representing social entities and the relationships or interactions among them. Friendships and interactions form, change, and disappear over time.
Static network: A network represented without changes to its nodes or edges over the period being studied. It omits the time-dependent structure that defines evolving networks.
Network rewiring: The alteration of a network’s edges while retaining some or all of its nodes. Rewiring changes connectivity without requiring the network to gain or lose nodes.
Degree distribution: A probability distribution describing how many edges connect to a node in a network. Its evolution shows whether growth changes the prevalence of hubs.
Epidemic model: A mathematical model of how an infectious disease spreads through a population. Changing contacts alter who can transmit infection and when.
Dynamic network: A system whose state changes over time, including networks when their structure or node states vary. The broader idea includes changing network structure as well as changing states on fixed links.
Temporal network: A network whose edges or interactions carry timing information, so their order and duration matter. It represents evolving connectivity with explicit times for interactions.
Clustering coefficient: A measure of how often a node’s neighbors are connected to one another. Tracking it over time reveals whether local cohesion changes as edges shift.