easygraph.functions.structural_holes.metrics module#

easygraph.functions.structural_holes.metrics.nodes_of_max_cc_without_shs(G, S)[source]#

Returns the number of nodes in the maximum connected component in graph GS. The experiment ml_metrics in [1]

Parameters:

  • G (easygraph.Graph or easygraph.DiGraph) –

  • S (list of int) – A list of nodes witch are structural hole spanners.

Returns:

G_S_nodes_of_max_CC – The number of nodes in the maximum connected component in graph GS.

Return type:

int

Examples

>>> G_t=eg.datasets.get_graph_blogcatalog()
>>> S_t=eg.AP_Greedy(G_t, 10000)
>>> maxx = nodes_of_max_cc_without_shs(G_t, S_t)
>>> print(maxx)

References

easygraph.functions.structural_holes.metrics.structural_hole_influence_index(G_original, S, C, model, variant=False, seedRatio=0.05, randSeedIter=10, countIterations=100, Directed=True)[source]#

Returns the SHII metric of each seed.

Parameters:

  • G_original (easygraph.Graph or easygraph.DiGraph) –

  • S (list of int) – A list of nodes which are structural hole spanners.

  • C (list of list) – Each list includes the nodes in one community.

  • model (string) – Propagation Model. Should be IC or LT.

  • variant (bool, default is False) – Whether returns variant SHII ml_metrics or not. variant SHII = # of the influenced outsider / # of the influenced insiders SHII = # of the influenced outsiders / # of the total influenced nodes

  • seedRatio (float, default is 0.05) – # of sampled seeds / # of nodes of the community that the given SHS belongs to.

  • randSeedIter (int, default is 10) – How many iterations to sample seeds.

  • countIterations (int default is 100) – Number of monte carlo simulations to be used.

  • Directed (bool, default is True) – Whether the graph is directed or not.

Returns:

seed_shii_pair – the SHII metric of each seed

Return type:

dict

Examples

# >>> structural_hole_influence_index(G, [3, 20, 9], Com, ‘LT’, seedRatio=0.1, Directed=False)

References

easygraph.functions.structural_holes.metrics.sum_of_shortest_paths(G, S)[source]#

Returns the difference between the sum of lengths of all pairs shortest paths in G and the one in GS. The experiment ml_metrics in [1]

Parameters:

  • G (easygraph.Graph or easygraph.DiGraph) –

  • S (list of int) – A list of nodes witch are structural hole spanners.

Returns:

differ_between_sum – The difference between the sum of lengths of all pairs shortest paths in G and the one in GS. C(G/S)-C(G)

Return type:

int

Examples

>>> G_t=eg.datasets.get_graph_blogcatalog()
>>> S_t=eg.AP_Greedy(G_t, 10000)
>>> diff = sum_of_shortest_paths(G_t, S_t)
>>> print(diff)

References