IMDB-MULTI数据集图神经网络资源-CSDN文库

共6个文件

txt：3个

pt：3个

神经网络

数据集

图神经网络

深度学习

机器学习

8 浏览量 2024-05-31 08:48:58 上传评论收藏 742KB ZIP 举报

IMDB-MULTI数据集是深度学习领域，特别是图神经网络（Graph Neural Networks, GNN）研究中的一个重要资源。这个数据集主要用于社交网络分析，它包含1500个图，每个图代表一个社交网络中的关系结构。这些图用于进行多类分类任务，具体来说，是三分类问题，意味着每个图可以被归类到三个类别之一。图神经网络是一种能够处理非欧几里得数据，如图形结构的深度学习模型。它们通过在图的节点之间传播和聚合信息来学习节点的表示，从而捕捉图形的拓扑结构。在IMDB-MULTI数据集中，每个图的节点可能代表社交网络中的用户，边则表示用户之间的互动或关系，例如朋友、关注、共同兴趣等。数据集的特点包括平均每个图有13个节点，这暗示着社交网络中用户的平均数量；而平均边数为65，这表示每个用户平均与其他约65个用户有互动。这样的数据特性使得GNN能够学习到节点间复杂的相互作用，对于理解社交网络的行为模式和群体属性具有很大的价值。使用IMDB-MULTI数据集时，首先需要对数据进行预处理，包括读取图结构、提取特征以及划分训练集、验证集和测试集。然后，构建GNN模型，常见的GNN模型有GCN（Graph Convolutional Network）、GAT（Graph Attention Network）和GIN（Graph Isomorphism Network）等。模型训练过程中，通过不断迭代更新节点的表示，直到模型收敛。通过比较模型预测的类别与实际类别，评估模型的性能，常用的评估指标有准确率、精确率、召回率和F1分数。此外，图神经网络在社交网络分析中有广泛的应用，比如社区检测、影响力最大化、异常检测、情感分析等。在IMDB-MULTI数据集上进行实验，可以帮助研究者更好地理解和改进GNN在处理社交网络数据时的性能，从而推动社交网络分析领域的技术发展。在进行深度学习和机器学习项目时，数据集的质量和适应性至关重要。IMDB-MULTI作为一个专门针对图结构的多分类数据集，为研究人员提供了丰富的实践机会，有助于探索图神经网络在社交网络分析领域的潜力。通过对这个数据集的深入研究和模型优化，我们可以期待在社交网络理解和预测方面取得更多突破。

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IMDB-MULTI.zip （6个子文件）

IMDB-MULTI

processed

data_deg+odeg100.pt 10.73MB

pre_transform.pt 431B

pre_filter.pt 431B

raw

IMDB-MULTI_graph_labels.txt 3KB

IMDB-MULTI_graph_indicator.txt 81KB

IMDB-MULTI_A.txt 4.49MB

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