Node2Vec实战

数据结构

两个两个连接的节点

1 2 2 3 4 5

主程序构建

G = nx.read_edgelist('../data/text.txt',create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)]) ## 构建模型 model = Node2Vec(G, walk_length=10, num_walks=80,p=0.25, q=4, workers=1, use_rejection_sampling=0) ## 训练 model.train(embed_size=4, window_size=5, iter=3) embeddings = model.get_embeddings() print(embeddings)

初始生成节点到节点的概率

def preprocess_transition_probs(self):'''Preprocessing of transition probabilities for guiding the random walks.'''####get_alias_edge这个函数是对每条边设定为二阶randomwalk的概率形式###这个函数的作用是生成每个边界的概率，同时会有alias_setup这个函数将概率进行转换，方便后面抽样G = self.Gis_directed = self.is_directedalias_nodes = {}for node in G.nodes():unnormalized_probs = [G[node][nbr]['weight'] for nbr in sorted(G.neighbors(node))]#读取每个邻点权重norm_const = sum(unnormalized_probs)###权重求和，作为公式中正则项常数的那个分母normalized_probs = [float(u_prob)/norm_const for u_prob in unnormalized_probs]###除以分母alias_nodes[node] = alias_setup(normalized_probs)alias_edges = {}triads = {}if is_directed:for edge in G.edges():alias_edges[edge] = self.get_alias_edge(edge[0], edge[1])else:for edge in G.edges():alias_edges[edge] = self.get_alias_edge(edge[0], edge[1])alias_edges[(edge[1], edge[0])] = self.get_alias_edge(edge[1], edge[0])self.alias_nodes = alias_nodesself.alias_edges = alias_edgesreturn

get_alias_edge是得到节点到节点的概率

def get_alias_edge(self, src, dst):####二阶ramdom walk#src是随机游走序列中的上一个节点，dst是当前节点'''Get the alias edge setup lists for a given edge.'''G = self.Gp = self.pq = self.qunnormalized_probs = []for dst_nbr in sorted(G.neighbors(dst)):if dst_nbr == src:unnormalized_probs.append(G[dst][dst_nbr]['weight']/p)elif G.has_edge(dst_nbr, src):unnormalized_probs.append(G[dst][dst_nbr]['weight'])else:unnormalized_probs.append(G[dst][dst_nbr]['weight']/q)norm_const = sum(unnormalized_probs)normalized_probs = [float(u_prob)/norm_const for u_prob in unnormalized_probs]return alias_setup(normalized_probs)

alias_setup ：输入概率，得到对应的两组数，方便后面的抽样调用

def alias_setup(probs):'''alias_setup的作用是根据二阶random walk输出的概率变成每个节点对应两个数，被后面的alias_draw函数所进行抽样'''K = len(probs)q = np.zeros(K)J = np.zeros(K, dtype=np.int)smaller = []larger = []for kk, prob in enumerate(probs):q[kk] = K*probif q[kk] < 1.0:smaller.append(kk)else:larger.append(kk)##kk是下标，表示哪些下标小while len(smaller) > 0 and len(larger) > 0:small = smaller.pop()##smaller自己也会减少最右边的值large = larger.pop()J[small] = largeq[large] = q[large] + q[small] - 1.0if q[large] < 1.0:smaller.append(large)else:larger.append(large)return J, q

alias_draw 抽样函数

def alias_draw(J, q):'''Draw sample from a non-uniform discrete distribution using alias sampling.'''K = len(J)kk = int(np.floor(np.random.rand()*K))if np.random.rand() < q[kk]:return kkelse:return J[kk]

node2vec_walk就是对于给定的长度，对于开始节点开始模拟这个节点的路径，涉及的函数都在上面提及

def node2vec_walk(self, walk_length, start_node):'''Simulate a random walk starting from start node.'''G = self.Galias_nodes = self.alias_nodesalias_edges = self.alias_edgeswalk = [start_node]######alias_draw这个函数是等于是根据二阶random walk概率选择下一个点while len(walk) < walk_length:cur = walk[-1]cur_nbrs = sorted(G.neighbors(cur))###G.neighbors(cur)得到cur一级关联的节点if len(cur_nbrs) > 0:if len(walk) == 1:####cur[0]walk.append(cur_nbrs[alias_draw(alias_nodes[cur][0], alias_nodes[cur][1])])else:prev = walk[-2]next = cur_nbrs[alias_draw(alias_edges[(prev, cur)][0], alias_edges[(prev, cur)][1])]walk.append(next)else:breakreturn walk

总结

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