Module temporal 

Temporal graph generation models

This module provides generators for temporal (time-evolving) networks beyond the basic activity-driven model already in temporal_graph. Three models are implemented:

• temporal_barabasi_albert – preferential attachment with timestamps. Each new node arrives at a specified time, contacts m existing nodes preferentially (higher-degree nodes are preferred), and the resulting contacts are recorded as temporal edges. Produces scale-free degree distributions with a well-defined temporal ordering.

• TemporalGraph (re-exported from temporal_graph) – the core temporal graph data structure used throughout this module.

• temporal_random_walk – time-respecting random walks on a temporal graph. A walk is time-respecting if each successive edge has a timestamp ≥ the previous edge’s timestamp. This is the standard foundation for temporal node embeddings and reachability analysis.

References

• Barabási, A.-L. & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.
• Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
• Pan, R. K. & Saramäki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84, 016105.

Re-exports

pub use crate::temporal_graph::activity_driven_model;

pub use crate::temporal_graph::activity_driven_model_seeded;

pub use crate::temporal_graph::burstiness;

Structs

TemporalWalk

A single time-respecting random walk on a temporal graph.

Functions

temporal_barabasi_albert

Generate a temporal Barabási–Albert (TBA) network with preferential attachment.

temporal_random_walk

Perform num_walks time-respecting random walks starting from source.