class EdgeMinibatchIterator
""" This minibatch iterator iterates over batches of sampled edges or random pairs of co-occuring edges. G -- networkx graph id2idx -- dict mapping node ids to index in feature tensor placeholders -- tensorflow placeholders object context_pairs -- if not none, then a list of co-occuring node pairs (from random walks) batch_size -- size of the minibatches max_degree -- maximum size of the downsampled adjacency lists n2v_retrain -- signals that the iterator is being used to add new embeddings to a n2v model fixed_n2v -- signals that the iterator is being used to retrain n2v with only existing nodes as context """
def __init__(self, G, id2idx, placeholders, context_pairs=None, batch_size=100, max_degree=25,
n2v_retrain=False, fixed_n2v=False, **kwargs) 中具體介紹以下：
1 self.nodes = np.random.permutation(G.nodes())
2 # 函數shuffle與permutation都是對原來的數組進行重新洗牌,即隨機打亂原來的元素順序
3 # shuffle直接在原來的數組上進行操作，改變原來數組的順序，無返回值
4 # permutation不直接在原來的數組上進行操作，而是返回一個新的打亂順序的數組，並不改變原來的數組。
1 self.adj, self.deg = self.construct_adj()
這里重點看construct_adj()函數。
1 def construct_adj(self): 2 adj = len(self.id2idx) * \ 3 np.ones((len(self.id2idx) + 1, self.max_degree)) 4 # 該矩陣記錄訓練數據中各節點的鄰居節點的編號 5 # 采樣只取max_degree個鄰居節點，采樣方法見下 6 # 同樣進行了行數加一操作 7 8 deg = np.zeros((len(self.id2idx),)) 9 # 該矩陣記錄了每個節點的度數 10 11 for nodeid in self.G.nodes(): 12 if self.G.node[nodeid]['test'] or self.G.node[nodeid]['val']: 13 continue 14 neighbors = np.array([self.id2idx[neighbor] 15 for neighbor in self.G.neighbors(nodeid) 16 if (not self.G[nodeid][neighbor]['train_removed'])]) 17 # Graph.neighbors() Return a list of the nodes connected to the node n. 18 # 在選取鄰居節點時進行了篩選，對於G.neighbors(nodeid) 點node的鄰居， 19 # 只取該node與neighbor相連的邊的train_removed = False的neighbor 20 # 也就是只取不是val, test的節點。 21 # neighbors得到了鄰居節點編號數列。 22 23 deg[self.id2idx[nodeid]] = len(neighbors) 24 # deg各位取值為該位對應nodeid的節點的度數， 25 # 也即經過上面篩選后得到的鄰居數 26 27 if len(neighbors) == 0: 28 continue 29 if len(neighbors) > self.max_degree: 30 neighbors = np.random.choice( 31 neighbors, self.max_degree, replace=False) 32 # range: neighbors; size = max_degree; replace: replace the origin matrix or not 33 # np.random.choice為選取size大小的數列 34 35 elif len(neighbors) < self.max_degree: 36 neighbors = np.random.choice( 37 neighbors, self.max_degree, replace=True) 38 # 經過choice隨機選取，得到了固定大小max_degree = 25的直接相連的鄰居數列 39 40 adj[self.id2idx[nodeid], :] = neighbors 41 # 把該node的鄰居數列，賦值給adj矩陣中對應nodeid位的向量。 42 return adj, deg
在construct_test_adj() 函數中，與上不同之處在於，可以直接得到鄰居而無需根據val/test/train_removed篩選.
1 neighbors = np.array([self.id2idx[neighbor] 2 for neighbor in self.G.neighbors(nodeid)])