python – 帮助优化我的社交网络演化模型

使用这个简单的模型,你可以说三个人的三位一体是“平衡的”或“不平衡的”,这取决于三合一边缘的产品是正面的还是负面的.

所以最后我要做的是实现一个ising类型模型.即如果新网络具有比翻转之前的网络更平衡的三边形(更低的能量),则随机边缘被翻转并保持新的关系,如果不是这种情况那么新关系仅以一定的概率保持.

好的,最后我的问题：我编写了以下代码,但是我的数据集包含~120k三元组,因此需要4天才能运行！

任何人都可以提供有关如何优化代码的任何提示吗？

谢谢,
理查德.

#Importing required librarys

try:
    import matplotlib.pyplot as plt
except:
    raise

import networkx as nx
import csv
import random
import math

def prod(iterable):
    p= 1
    for n in iterable:
        p *= n
    return p


def Sum(iterable):
    p= 0
    for n in iterable:
        p += n[3]
    return p


def CalcTriads(n):  
    firstgen=G.neighbors(n)
    Edges=[]
    Triads=[]

    for i in firstgen:
        Edges.append(G.edges(i))

    for i in xrange(len(Edges)):
        for j in range(len(Edges[i])):# For node n go through the list of edges (j) for the neighboring nodes (i) 
            if set([Edges[i][j][1]]).issubset(firstgen):# If the second node on the edge is also a neighbor of n (its in firstgen) then keep the edge.
                t=[n,Edges[i][j][0],Edges[i][j][1]]
                t.sort()
                Triads.append(t)# Add found nodes to Triads.

    new_Triads = []# Delete duplicate triads.
    for elem in Triads:
        if elem not in new_Triads:
            new_Triads.append(elem)
    Triads = new_Triads 

    for i in xrange(len(Triads)):# Go through list of all Triads finding the weights of their edges using G[node1][node2]. Multiply the three weights and append value to each triad.
            a=G[Triads[i][0]][Triads[i][1]].values()
            b=G[Triads[i][1]][Triads[i][2]].values()
            c=G[Triads[i][2]][Triads[i][0]].values()
            Q=prod(a+b+c)
            Triads[i].append(Q)

    return Triads



###### Import sorted edge data ######       
li=[]
with open('Sorted Data.csv','rU') as f:
    reader = csv.reader(f)
    for row in reader:
        li.append([float(row[0]),float(row[1]),float(row[2])])
G=nx.Graph()
G.add_weighted_edges_from(li)


for i in xrange(800000):
    e = random.choice(li)   # Choose random edge

    TriNei=[]
    a=CalcTriads(e[0]) # Find triads of first node in the chosen edge 
    for i in xrange(0,len(a)):
        if set([e[1]]).issubset(a[i]): # Keep triads which contain the whole edge (i.e. both nodes on the edge)
            TriNei.append(a[i])         
    preH=-Sum(TriNei) # Save the "energy" of all the triads of which the edge is a member


    e[2]=-1*e[2]# Flip the weight of the random edge and create a new graph with the flipped edge   
    G.clear()
    G.add_weighted_edges_from(li)


    TriNei=[]
    a=CalcTriads(e[0])  
    for i in xrange(0,len(a)):
        if set([e[1]]).issubset(a[i]):
            TriNei.append(a[i])
    postH=-Sum(TriNei)# Calculate the post flip "energy".   

    if postH<preH:# If the post flip energy is lower then the pre flip energy keep the change
        continue

    elif random.random() < 0.92: # If the post flip energy is higher then only keep the change with some small probability. (0.92 is an approximate placeholder for exp(-DeltaH)/exp(1) at the moment)
        e[2]=-1*e[2]