在中間時間步測量網絡屬性

Question

我必須在網絡增長的某些步驟（即在N = 100，N = 1000，N = 10000等）測量Barabasi Albert圖的某些屬性，如度分佈。 ）。我知道如何使用networkx生成這樣一個圖表，但我真的不清楚如何在增長過程中訪問這些屬性。

沒有代碼可以顯示你，我需要一個提示算法本身，一些例子將不勝感激。我使用Python 2.7，但如果有必要，我也很喜歡R。

Answer 1

我認爲你正在尋找的功能是Barbabasi Albert Graph function在netwrokx。在研究上述函數on this link（基於networkx文檔）的源代碼時，步驟在註釋中給出。您可以在每個循環的while source<n:循環中打印圖表的屬性，以獲得您所需的屬性。

Answer 2

基於@Mohammed卡希夫的回答，這裏是Barabasi阿爾伯特圖的經修改的源代碼，它返回鍵上的節點的數量和值的數組的度的詞典：

import random 
import networkx as nx 

def _random_subset(seq,m): 
    """ Return m unique elements from seq. 

    This differs from random.sample which can return repeated 
    elements if seq holds repeated elements. 
    """ 
    targets=set() 
    while len(targets)<m: 
     x=random.choice(seq) 
     targets.add(x) 
    return targets 

def barabasi_albert_graph_modified(n, m, seed=None): 

    if m < 1 or m >=n: 
     raise nx.NetworkXError(\ 
       "Barabási-Albert network must have m>=1 and m<n, m=%d,n=%d"%(m,n)) 
    if seed is not None: 
     random.seed(seed) 

    # Add m initial nodes (m0 in barabasi-speak) 
    G=nx.empty_graph(m) 
    G.name="barabasi_albert_graph(%s,%s)"%(n,m) 
    # Target nodes for new edges 
    targets=list(range(m)) 
    # List of existing nodes, with nodes repeated once for each adjacent edge 
    repeated_nodes=[] 
    # Start adding the other n-m nodes. The first node is m. 
    source=m 
    d = {} 
    while source<n: 
     # Add edges to m nodes from the source. 
     G.add_edges_from(zip([source]*m,targets)) 
     # Add one node to the list for each new edge just created. 
     repeated_nodes.extend(targets) 
     # And the new node "source" has m edges to add to the list. 
     repeated_nodes.extend([source]*m) 
     # Now choose m unique nodes from the existing nodes 
     # Pick uniformly from repeated_nodes (preferential attachement) 
     targets = _random_subset(repeated_nodes,m) 
     deg = np.array(list(G.degree().values())) 
     deg.sort() 
     d[G.number_of_nodes()] = deg 
     source += 1 
    return G,d 

讓我們嘗試它：

g,d = barabasi_albert_graph_modified(10,2) 
#out: 
#{3: array([1, 1, 2]), 
# 4: array([1, 2, 2, 3]), 
# 5: array([1, 2, 2, 3, 4]), 
# 6: array([1, 2, 2, 2, 4, 5]), 
# 7: array([1, 2, 2, 2, 3, 4, 6]), 
# 8: array([2, 2, 2, 2, 2, 3, 4, 7]), 
# 9: array([2, 2, 2, 2, 2, 2, 4, 5, 7]), 
# 10: array([2, 2, 2, 2, 2, 2, 3, 4, 5, 8])}