The quantity of data created in today’s society is large and increasing. Temporal networks are frequently connected with data and can help to deepen data analysis and show structural and historic alteration. Several network issues are based on relationships that require time, for instance modeling the influx of information over a distributed network, examining the reachability features of an airline time schedule, or analyzing the propagation of a disease through a population. In a temporal network, the graph represents the contacts that occur between edges at a given time. Large databases of items and their relationships are represented and analyzed using networks. Real-world networks include the temporal component: for example, interactions between objects have the timestamp and the duration. Temporal networks are growingly being utilized to simulate a wide range of systems that vary over time, such as human interaction structures that are subject to dynamic processes like epidemics. An essential feature of real-world networks that are represented within the temporal and spatial framework by sampling them. This review looks at a variety of disciplines where temporal graphs are used.
Topics
Review
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