Cora數據集介紹+python讀取

Cora數據集介紹+讀取

• 1. 數據集概括
• 2. 數據集組成
  • 2.1 Content文件
  • 2.2 Cites文件
• 3. 下載地址
• 4. 如何讀取（python）

1. 數據集概括
Cora數據集由機器學習論文組成，是近年來圖深度學習很喜歡使用的數據集。在數據集中，論文分爲以下七類之一:

基於案例
遺傳算法
神經網絡
概率方法
強化學習
規則學習
理論
論文的選擇方式是，在最終語料庫中，每篇論文引用或被至少一篇其他論文引用。整個語料庫中有2708篇論文。

在詞幹堵塞和去除詞尾後，只剩下1433個獨特的單詞。文檔頻率小於10的所有單詞都被刪除。

2. 數據集組成
目錄包含兩個文件:

2.1 Content文件
.content文件包含以下格式的論文描述:

<paper_id> <word_attributes>+ <class_label>

這個位置需要重新解讀一下，在原作者的表達下稍作調整：這個描述的意思是總共每一行的數據是1435個，1+1433+1，因此

(在下面的代碼中idx_features_labels[:,1-1],指的是將1+1433+1中的中間的1433個特徵值給取出來，從宏觀上整體應該是一個二維數組，只不過第二維中有1435個值,從數學上看是1435維，然後有2708個點)

每行的第一個條目包含紙張的唯一字符串標識，後跟二進制值，指示詞彙中的每個單詞在文章中是存在(由1表示)還是不存在(由0表示)。

最後，該行的最後一個條目包含紙張的類別標籤。因此數據集的feature應該爲2709×14332709 \times 14332709×1433維度。第一行爲idx，最後一行爲label。

部分數據

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......

2.2 Cites文件
那個.cites文件包含語料庫的引用’圖’。每行以以下格式描述一個鏈接:

<被引論文編號> <引論文編號>

每行包含兩個紙質id。第一個條目是被引用論文的標識，第二個標識代表包含引用的論文。鏈接的方向是從右向左。

如果一行由“論文1 論文2”表示，則鏈接是“論文2 - >論文1”。可以通過論文之間的索引關係建立鄰接矩陣adj

部分數據
 

35	1033
35	103482
35	103515
35	1050679
35	1103960
35	1103985
35	1109199
35	1112911
......

3. 下載地址

https://linqs.soe.ucsc.edu/data

4. 如何讀取（python）

def load_data(path="./data/cora/", dataset="cora"):
    """Load citation network dataset (cora only for now)"""
    print('Loading {} dataset...'.format(dataset))

    idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset), dtype=np.dtype(str))
    features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
    labels = encode_onehot(idx_features_labels[:, -1])

    # build graph
    idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
    idx_map = {j: i for i, j in enumerate(idx)}  #這是一個字典表達式！
    edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), dtype=np.int32)
    edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape)
    adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32)

    # build symmetric adjacency matrix
    adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)

    features = normalize_features(features)
    adj = normalize_adj(adj + sp.eye(adj.shape[0]))

    idx_train = range(140)
    idx_val = range(200, 500)
    idx_test = range(500, 1500)

    adj = torch.FloatTensor(np.array(adj.todense()))
    features = torch.FloatTensor(np.array(features.todense()))
    labels = torch.LongTensor(np.where(labels)[1])

    idx_train = torch.LongTensor(idx_train)
    idx_val = torch.LongTensor(idx_val)
    idx_test = torch.LongTensor(idx_test)

    return adj, features, labels, idx_train, idx_val, idx_test