首个专用于图机器学习的二分类社交网络数据集，填补领域空白。
A Binary Classification Social Network Dataset for Graph Machine Learning
- 构建了表格式与图格式双版本的二分类社交网络数据集
- 各类模型在该数据集上F1得分介于67.66至70.15之间
- 适合评估传统与前沿图学习方法的分类性能
社交网络在图结构应用中具有广泛前景，现有基准数据集多为引用、共现或电商网络，类别数在3到15之间。然而，当前缺乏专门用于图机器学习的二分类社交网络基准数据集。本文填补这一空白，提出二分类社交网络数据集（BiSND），专为图机器学习中的二分类任务设计。我们以表格和图两种格式提供BiSND，以验证其在经典与先进机器学习方法上的鲁棒性。采用多种分类器进行评估，包括四种传统算法（决策树、K近邻、随机森林、XGBoost）、一个深度神经网络（多层感知机）、一个图神经网络（图卷积网络）以及三种最先进的图对比学习方法（BGRL、GRACE、DAENS）。实验结果表明，BiSND适用于分类任务，各模型F1分数在67.66至70.15之间，展现出未来优化的潜力。
原文摘要 · Abstract (English)
Social networks have a vast range of applications with graphs. The available benchmark datasets are citation, co-occurrence, e-commerce networks, etc, with classes ranging from 3 to 15. However, there is no benchmark classification social network dataset for graph machine learning. This paper fills the gap and presents the Binary Classification Social Network Dataset (\textit{BiSND}), designed for graph machine learning applications to predict binary classes. We present the BiSND in \textit{tabular and graph} formats to verify its robustness across classical and advanced machine learning. We employ a diverse set of classifiers, including four traditional machine learning algorithms (Decision Trees, K-Nearest Neighbour, Random Forest, XGBoost), one Deep Neural Network (multi-layer perceptrons), one Graph Neural Network (Graph Convolutional Network), and three state-of-the-art Graph Contrastive Learning methods (BGRL, GRACE, DAENS). Our findings reveal that BiSND is suitable for classification tasks, with F1-scores ranging from 67.66 to 70.15, indicating promising avenues for future enhancements.
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