algorithms

Algorithms for temporal path calculation and graph metrics.

The functions and submodules in this module allow to compute time-respecting or causal paths in temporal graphs and to calculate (temporal) and higher-order graph metrics like centralities.

Example

# Import pathpyG and configure your torch device if you want to use GPU .
import pathpyG as pp
pp.config['torch']['device'] = 'cuda'

# Generate a toy example for a temporal graph.
g = pp.TemporalGraph.from_edge_list([
    ('b', 'c', 2),
    ('a', 'b', 1),
    ('c', 'd', 3),
    ('d', 'a', 4),
    ('b', 'd', 2),
    ('d', 'a', 6),
    ('a', 'b', 7)
])

# Extract DAG capturing causal interaction sequences in temporal graph.
e_i = pp.algorithms.lift_order_temporal(g, delta=1)
dag = pp.Graph.from_edge_index(e_i)
print(dag)

# Calculate shortest time-respecting pathas
dist, pred = pp.algorithms.temporal.temporal_shortest_paths(g, delta=1)