标题： 对比级联图学习用于分类真实和合成信息扩散模式 显示英文标题

标题： Contrastive Cascade Graph Learning for Classifying Real and Synthetic Information Diffusion Patterns

摘要： 通过社交媒体传播了各种信息，大规模传播的内容可能对现实世界产生实际影响。 为了遏制有害内容的传播并促进可靠信息的传播，级联图挖掘的研究引起了越来越多的关注。 该领域的一个有前景的方法是对比级联图学习（CCGL）。 级联图挖掘中的一个重要任务是级联分类，这涉及根据其结构特征对级联图进行分类。 尽管预期CCGL对该任务有效，但其性能尚未得到全面评估。 本研究旨在探讨CCGL在级联分类中的有效性。 我们的研究结果表明，CCGL在捕捉级联图中的平台和模型特定结构模式方面表现出色，突显了其在一系列下游信息扩散分析任务中的潜力。 显示英文摘要

摘要： A wide variety of information is disseminated through social media, and content that spreads at scale can have tangible effects on the real world. To curb the spread of harmful content and promote the dissemination of reliable information, research on cascade graph mining has attracted increasing attention. A promising approach in this area is Contrastive Cascade Graph Learning (CCGL). One important task in cascade graph mining is cascade classification, which involves categorizing cascade graphs based on their structural characteristics. Although CCGL is expected to be effective for this task, its performance has not yet been thoroughly evaluated. This study aims to investigate the effectiveness of CCGL for cascade classification. Our findings demonstrate the strong performance of CCGL in capturing platform- and model-specific structural patterns in cascade graphs, highlighting its potential for a range of downstream information diffusion analysis tasks.