Python 基于优先连接算法的无标度网络

我很难理解这段代码的作用。请有人一步一步地检查代码，并解释它是如何工作的以及它在做什么

def scale_free(n,m):
    if m < 1 or  m >=n: 
        raise nx.NetworkXError("Preferential attactment algorithm must have m >= 1"
                               " and m < n, m = %d, n = %d" % (m, n)) 
    # Add m initial nodes (m0 in barabasi-speak)
    G=nx.empty_graph(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
    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)
        source += 1
    return G

def无刻度（n，m）：
如果m<1或m>=n：
提高nx.NetworkXError（“优先附加算法必须具有m>=1”
“和m而source的第一部分确保
m
至少为1和
n>m

def scale_free(n,m):
    if m < 1 or  m >=n: 
        raise nx.NetworkXError("Preferential attactment algorithm must have m >= 1"
                               " and m < n, m = %d, n = %d" % (m, n)) 

现在，它将开始一次添加1个新节点，并根据各种规则将它们连接到现有节点。它首先创建一组“目标”，其中包含无边图中的所有节点

    # 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

现在，它将一次添加每个节点1。当它这样做时，它会将带有边的新节点添加到先前现有节点的
m
。那些
m
以前的节点已存储在名为
targets
的列表中

    while source<n:

现在，它将决定在添加下一个节点时谁将获得这些边。它应该以与其程度成比例的概率来选择它们，其方法是通过一个列表
重复的\u节点
，每个节点在每条边上出现一次。然后，它从中随机选择一组
m
节点作为新目标。根据
\u random\u subset
的定义方式，它可能会或可能不会多次选择同一节点作为同一步骤中的目标

        # 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)
        source += 1
    return G

您好@killerownage2006，注释中对代码进行了很好的记录。你有什么特别的问题吗？非常感谢！这有助于我的理解：）
        # 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)
        source += 1
    return G