Cora数据集介绍+读取

• 1. 数据集概括
• 2. 数据集组成
  • 2.1 Content文件
  • 2.2 Cites文件
• 3. 下载地址
• 4. 如何读取（python）

1. 数据集概括
Cora数据集由机器学习论文组成，是近年来图深度学习很喜欢使用的数据集。在数据集中，论文分为以下七类之一:

基于案例
遗传算法
神经网络
概率方法
强化学习
规则学习
理论
论文的选择方式是，在最终语料库中，每篇论文引用或被至少一篇其他论文引用。整个语料库中有2708篇论文。

在词干堵塞和去除词尾后，只剩下1433个独特的单词。文档频率小于10的所有单词都被删除。

2. 数据集组成
目录包含两个文件:

2.1 Content文件
.content文件包含以下格式的论文描述:

<paper_id> <word_attributes>+ <class_label>

这个位置需要重新解读一下，在原作者的表达下稍作调整：这个描述的意思是总共每一行的数据是1435个，1+1433+1，因此

(在下面的代码中idx_features_labels[:,1-1],指的是将1+1433+1中的中间的1433个特征值给取出来，从宏观上整体应该是一个二维数组，只不过第二维中有1435个值,从数学上看是1435维，然后有2708个点)

每行的第一个条目包含纸张的唯一字符串标识，后跟二进制值，指示词汇中的每个单词在文章中是存在(由1表示)还是不存在(由0表示)。

最后，该行的最后一个条目包含纸张的类别标签。因此数据集的feature应该为2709×14332709 \times 14332709×1433维度。第一行为idx，最后一行为label。

部分数据

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......

2.2 Cites文件
那个.cites文件包含语料库的引用’图’。每行以以下格式描述一个链接:

<被引论文编号> <引论文编号>

每行包含两个纸质id。第一个条目是被引用论文的标识，第二个标识代表包含引用的论文。链接的方向是从右向左。

如果一行由“论文1 论文2”表示，则链接是“论文2 - >论文1”。可以通过论文之间的索引关系建立邻接矩阵adj

部分数据
 

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

3. 下载地址

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

4. 如何读取（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)}  #这是一个字典表达式！
    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