As the title implies, I'm trying to write a function that will calculate the number of cycles any inputted node is part of. I found a helpful video which explains the theory behind an algorithm to find cycles, but I'm having trouble understanding how to implement it using networkX rather than the data structure that site is using. I couldn't understand the white/grey/etc set concept as well to traverse the network and find cycles.
My function parameters/structure:
def feedback_loop_counter(G, node):
c = 0
calculate all cycles in the network
for every cycle node is in, increment c by 1
return c
The network has input and output nodes too, and I'm unclear how those play into calculating cycles
This is my input network:
import networkx as nx
import matplotlib.pyplot as plt
G=nx.DiGraph()
molecules = ["CD40L", "CD40", "NF-kB", "XBP1", "Pax5", "Bach2", "Irf4", "IL-4", "IL-4R", "STAT6", "AID", "Blimp1", "Bcl6", "ERK", "BCR", "STAT3", "Ag", "STAT5", "IL-21R", "IL-21", "IL-2", "IL-2R"]
Bcl6 = [("Bcl6", "Bcl6"), ("Bcl6", "Blimp1"), ("Bcl6", "Irf4")]
STAT5 = [("STAT5", "Bcl6")]
IL_2R = [("IL-2R", "STAT5")]
IL_2 = [("IL-22", "IL-2R")]
BCR = [("BCR", "ERK")]
Ag = [("Ag", "BCR")]
CD40L = [("CD40L", "CD40")]
CD40 = [("CD40", "NF-B")]
NF_B = [("NF-B", "Irf4"), ("NF-B", "AID")]
Irf4 = [("Irf4", "Bcl6"), ("Irf4", "Pax5"), ("Irf4", "Irf4"), ("Irf4", "Blimp1")]
ERK = [("ERK", "Bcl6"), ("ERK", "Blimp1"), ("ERK", "Pax5")]
STAT3 = [("STAT3", "Blimp1")]
IL_21 = [("IL-21", "IL-21R")]
IL_21R = [("IL-21R", "STAT3")]
IL_4R = [("IL-4R", "STAT6")]
STAT6 = [("STAT6", "AID"), ("STAT6", "Bcl6")]
Bach2 = [("Bach2", "Blimp1")]
IL_4 = [("IL-4", "IL-4R")]
Blimp1 = [("Blimp1", "Bcl6"), ("Blimp1", "Bach2"), ("Blimp1", "Pax5"), ("Blimp1", "AID"), ("Blimp1", "Irf4")]
Pax5 = [("Pax5", "Pax5"), ("Pax5", "AID"), ("Pax5", "Bcl6"), ("Pax5", "Bach2"), ("Pax5", "XBP1"), ("Pax5", "ERK"), ("Pax5", "Blimp1")]
edges = Bcl6 + STAT5 + IL_2R + IL_2 + BCR + Ag + CD40L + CD40 + NF_B + Irf4 +
ERK + STAT3 + IL_21 + IL_21R + IL_4R + STAT6 + Bach2 + IL_4 + Blimp1 + Pax5
G.add_nodes_from(molecules)
G.add_edges_from(edges)
sources = ["Ag", "CD40L", "IL-2", "IL-21", "IL-4"]
targets = ["XBP1", "AID"]
I'm going to write my answer under the assumption you are interested in "simple cycles", that is, cycles whose only repeated node is the first/last node.
Take those nodes that have edges to a node u (the "input nodes"). Then use the networkx command all_simple_paths to find all simple paths from u to each of the input nodes. Each of these becomes a simple cycle.