A Binary Classification Social Network Dataset for Graph Machine Learning

• URL: http://arxiv.org/abs/2503.02397v1
• Date: Tue, 04 Mar 2025 08:40:42 GMT
• Title: A Binary Classification Social Network Dataset for Graph Machine Learning
• Authors: Adnan Ali, Jinglong Li, Huanhuan Chen, AlMotasem Bellah Al Ajlouni,
• Abstract summary: There is no benchmark classification social network dataset for graph machine learning.<n>We present the Binary Classification Social Network dataset (textitBiSND), designed for graph machine learning applications to predict binary classes.<n>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.
• Score: 15.282729507317065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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