A Python library to compute trophic coherence in directed graphs
Example:
import networkx as nx import TrophicCoherence
G = nx.DiGraph()
G.add_edges_from([(1, 10), (2, 10), (3, 20), (4, 20), (10, 100), (20, 100)])
_, G = TrophicCoherence.compute_trophic_levels(G)
nx.draw_planar(G, with_labels=True, node_color="r", alpha = 0.7, arrowsize=20, node_size=500, font_weight="bold")
coherence = TrophicCoherence.coherence_parameter(G, TrophicCoherence.trophic_diff(G))
print("Coherence of the network: ", coherence)
Coherence of the network: 0.0
G = nx.DiGraph()
G.add_edges_from([(1, 100), (20, 10), (3, 20), (4, 10), (4, 20), (10, 100), (4, 100), (100,20), (100,1), (1,20)])
_, G = TrophicCoherence.compute_trophic_levels(G)
nx.draw_planar(G, with_labels=True, node_color="r", alpha = 0.7, arrowsize=20, node_size=500, font_weight="bold")
coherence = TrophicCoherence.coherence_parameter(G, TrophicCoherence.trophic_diff(G))
print("Coherence of the network: ", coherence)
Coherence of the network: 1.65