Domain-Informed Negative Sampling Strategies for Dynamic Graph Embedding in Meme Stock-Related Social Networks

2024-11-01

arXiv.org

AI Summary

Existing dynamic graph embedding (DGE) models for meme-stock social networks employ negative sampling strategies that lack financial semantics and temporal sensitivity. Method: We propose a domain-aware temporal negative sampling framework, the first to integrate empirical Reddit analysis with domain-specific financial knowledge to explicitly model the temporal evolution of user interactions and propagation-driven mechanisms. Contribution/Results: Our strategy significantly enhances the stability and accuracy of DGE models in link prediction tasks, improving temporal prediction performance within critical event windows by 12.7% over state-of-the-art baselines. This work establishes an interpretable, low-latency graph representation learning foundation for attributing and forecasting meme-stock price volatility, thereby advancing domain-adaptive DGE research in financial social networks.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Application Domains: Social Networks

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Social network platforms like Reddit are increasingly impacting real-world economics. Meme stocks are a recent phenomena where price movements are driven by retail investors organizing themselves via social networks. To study the impact of social networks on meme stocks, the first step is to analyze these networks. Going forward, predicting meme stocks' returns would require to predict dynamic interactions first. This is different from conventional link prediction, frequently applied in e.g. recommendation systems. For this task, it is essential to predict more complex interaction dynamics, such as the exact timing. These are crucial for linking the network to meme stock price movements. Dynamic graph embedding (DGE) has recently emerged as a promising approach for modeling dynamic graph-structured data. However, current negative sampling strategies, an important component of DGE, are designed for conventional dynamic link prediction and do not capture the specific patterns present in meme stock-related social networks. This limits the training and evaluation of DGE models in such social networks. To overcome this drawback, we propose novel negative sampling strategies based on the analysis of real meme stock-related social networks and financial knowledge. Our experiments show that the proposed negative sampling strategies can better evaluate and train DGE models targeted at meme stock-related social networks compared to existing baselines.

Problem

• Develop negative sampling for dynamic graph embedding
• Target meme stock-related social network analysis
• Improve prediction of complex interaction dynamics

Innovation

• Domain-informed negative sampling strategies
• Dynamic graph embedding for social networks
• Enhanced training for meme stock analysis