Module temporal_graph 

Temporal and Dynamic Graphs

This module provides data structures and algorithms for temporal (dynamic) graphs, where edges carry continuous-time timestamps rather than discrete time intervals. It implements the stream-of-interactions model commonly used in the analysis of real-world contact networks, communication networks, and social interaction data.

Key Concepts

• Temporal edge: a directed or undirected contact (u, v, t, w) at time t
• Time-respecting path: a sequence of edges whose timestamps are non-decreasing
• Temporal betweenness: how often a node lies on optimal time-respecting paths
• Burstiness: statistical irregularity of inter-event times (Goh–Barabási 2008)
• Activity-driven model: synthetic generative model (Perra et al. 2012)

References

• Holme & Saramäki, “Temporal networks”, Physics Reports 519(3), 2012.
• Goh & Barabási, “Burstiness and memory in complex systems”, EPL 81(4), 2008.
• Perra et al., “Activity driven modeling of time-varying networks”, Sci. Rep. 2012.

Structs

TemporalEdge

A single temporal edge with a continuous-time timestamp.

TemporalGraph

A temporal (dynamic) graph stored as a sorted stream of timed edge contacts.

Functions

activity_driven_model

Generate a synthetic temporal graph using the Activity-Driven Model (Perra et al. 2012).

activity_driven_model_seeded

Convenience wrapper: run activity_driven_model with a seeded ChaCha20 RNG.

burstiness

Compute the Goh–Barabási burstiness coefficient for a sequence of event times belonging to a single node.