Networkx - 从检测到的社区生成的子图的熵

Question

. Answers to this question are eligible for a +100 reputation bounty. 8-Bit Borges wants to to this question.

I have 4 functions for some statistical calculations in complex networks analysis.

import networkx as nx
import numpy as np
import math
from astropy.io import fits 

Degree distribution of graph:

def degree_distribution(G):
    vk = dict(G.degree())
    vk = list(vk.values()) # we get only the degree values
    maxk = np.max(vk)
    mink = np.min(min)
    kvalues= np.arange(0,maxk+1) # possible values of k
    Pk = np.zeros(maxk+1) # P(k)
    for k in vk:
        Pk[k] = Pk[k] + 1
    Pk = Pk/sum(Pk) # the sum of the elements of P(k) must to be equal to one
    
    return kvalues,Pk

Community detection of graph:

def calculate_community_modularity(graph):
    
    communities = greedy_modularity_communities(graph) # algorithm
    modularity_dict = {} # Create a blank dictionary

    for i,c in enumerate(communities): # Loop through the list of communities, keeping track of the number for the community
        for name in c: # Loop through each neuron in a community
            modularity_dict[name] = i # Create an entry in the dictionary for the neuron, where the value is which group they belong to.

    nx.set_node_attributes(graph, modularity_dict, 'modularity')
    
    print (graph_name)
    for i,c in enumerate(communities): # Loop through the list of communities
        #if len(c) > 2: # Filter out modularity classes with 2 or fewer nodes
            print('Class '+str(i)+':', len(c)) # Print out the classes and their member numbers
    return modularity_dict
            

Modularity score of graph:

def modularity_score(graph):
    return nx_comm.modularity(graph, nx_comm.label_propagation_communities(graph))

and finally graph Entropy:

def shannon_entropy(G):
    k,Pk = degree_distribution(G)
    H = 0
    for p in Pk:
        if(p > 0):
            H = H - p*math.log(p, 2)
    return H

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QUESTION

What I would like to achieve now is find local entropy for each community (turned into a subgraph), with preserved edges information.

Is this possible? How so?

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EDIT :

Matrix being used is in this link:

dataset

with fits.open('mind_dataset/matrix_CEREBELLUM_large.fits') as data:
    matrix = pd.DataFrame(data[0].data.byteswap().newbyteorder())

and then turn the adjacency matrix into a graph, 'graph', or 'G' like so:

def matrix_to_graph(matrix):
    from_matrix = matrix.copy()
    to_numpy = from_matrix.to_numpy()
    G = nx.from_numpy_matrix(to_numpy)
    return G 

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EDIT 2 :

Based on the proposed answer bellow I have created another function:

def community_entropy(modularity_dict):
    communities = {}

    #create communities as lists of nodes
    for node, community in modularity_dict.items():
        if community not in communities.keys():
            communities[community] = [node]
        else:
            communities[community].append(node)

    print(communities)
    #transform lists of nodes to actual subgraphs
    for subgraph, community in communities.items():
        communities[community] = nx.Graph.subgraph(subgraph)
        
    local_entropy = {}
    for subgraph, community in communities.items():
        local_entropy[community] = shannon_entropy(subgraph)
        
    return local_entropy

and:

cerebellum_graph = matrix_to_graph(matrix)
modularity_dict_cereb = calculate_community_modularity(cerebellum_graph)
community_entropy_cereb = community_entropy(modularity_dict_cereb)

But it throws the error:

TypeError: subgraph() missing 1 required positional argument: 'nodes'

---

Any help?

Answer 1

It looks like, in calculate_community_modularity , you use greedy_modularity_communities to create a dict, modularity_dict , which maps a node in your graph to a community . If I understand correctly, you can take each subgraph community in modularity_dict and pass it into shannon_entropy to calculate the entropy for that community.

---

pseudo code

this is pseudo code, so there may be some errors. This should convey the principle, though.

after running calculate_community_modularity , you have a dict like this, where the key is each node, and the value is that which the community belongs to

modularity_dict = {node_1: community_1, node_2: community_1, node_3: community_2}

I've never used nx , but it looks like you can extract a subgraph based on a list of nodes . So you would iterate through your dict, and create a list of nodes for each community. then you would use that list of nodes to extract the actual nx subgraph for that community.

communities = {}

#create communities as lists of nodes
for node, community in modularity_dict.iteritems():
    if community not in communities.keys():
        communities[community] = [node]
    else:
        communities[community].append(node)

#transform lists of nodes to actual subgraphs
for subgraph, community in communities.iteritems():
    communities[community] = networkx.Graph.subgraph(subgraph)

now that communities is a dict with key of the community id, and a value of the nx subgraph which defines that community, you should be able to run those subgraphs through shannon_entropy , as the type of the subgraphs is the same as the type of your original graph

local_entropy = {}
for subgraph, community in communities.iteritems():
    local_entropy[community] = shannon_entropy(subgraph)

Answer 2

Using the code I provided as an answer to your question here to create graphs from communities. You can first create different graphs for each of your communities (based on the community edge attribute of your graph). You can then compute the entropy for each community with your shannon_entropy and degree_distribution function.

See code below based on the karate club example you provided in your other question referenced above:

import networkx as nx
import networkx.algorithms.community as nx_comm
import matplotlib.pyplot as plt
import numpy as np
import math

def degree_distribution(G):
    vk = dict(G.degree())
    vk = list(vk.values()) # we get only the degree values
    maxk = np.max(vk)
    mink = np.min(min)
    kvalues= np.arange(0,maxk+1) # possible values of k
    Pk = np.zeros(maxk+1) # P(k)
    for k in vk:
        Pk[k] = Pk[k] + 1
    Pk = Pk/sum(Pk) # the sum of the elements of P(k) must to be equal to one
    
    return kvalues,Pk

def shannon_entropy(G):
    k,Pk = degree_distribution(G)
    H = 0
    for p in Pk:
        if(p > 0):
            H = H - p*math.log(p, 2)
    return H


G = nx.karate_club_graph()

# Find the communities
communities = sorted(nx_comm.greedy_modularity_communities(G), key=len, reverse=True)

# Count the communities
print(f"The club has {len(communities)} communities.")

'''Add community to node attributes'''
for c, v_c in enumerate(communities):
    for v in v_c:
        # Add 1 to save 0 for external edges
        G.nodes[v]['community'] = c + 1

'''Find internal edges and add their community to their attributes'''
for v, w, in G.edges:
    if G.nodes[v]['community'] == G.nodes[w]['community']:
        # Internal edge, mark with community
        G.edges[v, w]['community'] = G.nodes[v]['community']
    else:
        # External edge, mark as 0
        G.edges[v, w]['community'] = 0


N_coms=len(communities)
edges_coms=[]#edge list for each community
coms_G=[nx.Graph() for _ in range(N_coms)] #community graphs
colors=['tab:blue','tab:orange','tab:green']
fig=plt.figure(figsize=(12,5))

for i in range(N_coms):
  edges_coms.append([(u,v,d) for u,v,d in G.edges(data=True) if d['community'] == i+1])#identify edges of interest using the edge attribute
  coms_G[i].add_edges_from(edges_coms[i]) #add edges

ent_coms=[shannon_entropy(coms_G[i]) for i in range(N_coms)] #Compute entropy
for i in range(N_coms):
  plt.subplot(1,3,i+1)#plot communities
  plt.title('Community '+str(i+1)+ ', entropy: '+str(np.round(ent_coms[i],1)))
  pos=nx.circular_layout(coms_G[i])
  nx.draw(coms_G[i],pos=pos,with_labels=True,node_color=colors[i])  

And the output gives: