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

2025-07-16

AI Summary

This work addresses cascade graph classification across multiple platforms and generative models, presenting the first systematic evaluation of Cascade Contrastive Graph Learning (CCGL) to discern fine-grained structural differences between genuine and synthetic information diffusion. Methodologically, we propose a self-supervised framework integrating contrastive learning with graph neural networks, leveraging intra-cascade and inter-cascade structural contrast to enhance embedding discriminability—without requiring manual annotations—while capturing platform- and model-specific propagation topologies. Experiments on multiple real-world datasets demonstrate that CCGL significantly outperforms conventional graph classification baselines, achieving state-of-the-art performance in distinguishing authentic from synthetic diffusion patterns. These results validate CCGL’s strong representational capacity for heterogeneous structural motifs in cascade graphs and establish a novel, interpretable paradigm for misinformation detection.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

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.

Problem

• Evaluating CCGL for classifying real vs synthetic diffusion patterns
• Assessing CCGL's performance in cascade graph structural classification
• Exploring CCGL's ability to capture platform-specific diffusion characteristics

Innovation

• Contrastive Cascade Graph Learning for classification
• Captures platform-specific structural patterns
• Analyzes information diffusion using cascade graphs