reticula 0.9.2

Analyse temporal network and hypergraphs efficiently.

Python bindings for Reticula

Installation

The library offers pre-compiled Wheels for x64 Windows, MacOS and Linux. The library currently supports Python version 3.8 or newer.

$ pip install reticula

Installing from source

Alternatively you can install the library from source:

Clone the library:

$ git clone https://github.com/arashbm/reticula-python.git

Build the Wheel:

$ cd reticula-python
$ pip install .

Note that compiling from source requires an unbelievable amount (> 40GB) of RAM.

Basic examples

Generate a random static network and investigate:

>>> import reticula as ret
>>> state = ret.mersenne_twister(42)  # create a pseudorandom number generator
>>> g = ret.random_gnp_graph[ret.int64](n=100, p=0.02, random_state=state)
>>> g
<undirected_network[int64] with 100 verts and 110 edges>
>>> g.vertices()
[0, 1, 2, 3, .... 99]
>>> g.edges()
[undirected_edge[int64](0, 16), undirected_edge[int64](0, 20),
   undirected_edge[int64](0, 31), undirected_edge[int64](0, 51), ...]
>>> ret.connected_components(g)
[<component[int64] of 1 nodes: {9}>, <component[int64] of 1 node {33}>, ...]
>>> lcc = max(ret.connected_components(g), key=len)
>>> lcc
<component[int64] of 93 nodes: {99, 96, 95, 94, ...}>
>>> g2 = ret.vertex_induced_subgraph(g, lcc)
>>> g2
<undirected_network[int64] with 93 verts and 109 edges>

A more complete example of static network percolation analysis, running on multiple threads, can be found in examples/static_network_percolation/

Create a random fully-mixed temporal network and calculate simple (unconstrained) reachability from node 0 at time 0 to all nodes and times.

>>> import reticula as ret
>>> state = ret.mersenne_twister(42)
>>> g = ret.random_fully_mixed_temporal_network[ret.int64](\
...    size=100, rate=0.01, max_t=1024, random_state=state)
>>> adj = ret.temporal_adjacency.simple[\
...    ret.undirected_temporal_edge[ret.int64, ret.double]]()
>>> cluster = ret.out_cluster(\
...    temporal_network=g, temporal_adjacency=adj, vertex=0, time=0.0)
>>> cluster
<temporal_cluster[undirected_temporal_edge[int64, double],
  simple[undirected_temporal_edge[int64, double]]] with volume 100
  and lifetime (0 1.7976931348623157e+308]>
>>> cluster.covers(vertex=12, time=100.0)
True

>>> # Let's see all intervals where vert 15 is reachable from vert 0 at t=0.0:
>>> list(cluster.interval_sets()[15])
[(3.099055278145548, 1.7976931348623157e+308)]

Let's now try limited waiting-time (with dt = 5.0) reachability:

>>> import reticula as ret
>>> state = ret.mersenne_twister(42)
>>> g = ret.random_fully_mixed_temporal_network[int64](\
...   size=100, rate=0.01, max_t=1024, random_state=state)
>>> adj = ret.temporal_adjacency.limited_waiting_time[\
...   ret.undirected_temporal_edge[ret.int64, ret.double]](dt=5.0)
>>> cluster = ret.out_cluster(\
...  temporal_network=g, temporal_adjacency=adj, vertex=0, time=0.0)
>>> cluster
<temporal_cluster[undirected_temporal_edge[int64, double],
  limited_waiting_time[undirected_temporal_edge[int64, double]]] with
  volume 100 and lifetime (0 1028.9972186553928]>
>>> cluster.covers(vertex=15, time=16.0)
True
>>> list(cluster.interval_sets()[15])
[(3.099055278145548, 200.17866501023616),
  (200.39858803326402, 337.96139372380003),
  ...
  (1017.5258263596586, 1028.9149586273347)]

>>> # Total "human-hours" of reachability cluster
>>> cluster.mass()
101747.97444555275

>>> # Survival time of the reachability cluster
>>> cluster.lifetime()
(0.0, 1028.9972186553928)