Scalability

Scalability refers to the ability of systems, algorithms, and models to handle increasing amounts of data and computation while maintaining acceptable performance. In misinformation detection, scalability is critical: social networks contain billions of users and posts; news datasets span years and multiple languages; detection models must handle diverse content types (text, images, video).

Scalability challenges arise across multiple dimensions: memory (fitting models and data in machine memory), computation (training time on large datasets), and communication (inter-machine overhead in distributed systems). Solutions include efficient algorithms, distributed computation, sampling, model compression, and caching.

Key papers

• The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey — surveys scalability techniques in distributed GNN training including partitioning, sampling, and communication strategies

Related topics

• Distributed Systems (scaling via multiple machines)
• Graph Processing (scaling graph algorithms)
• Graph Neural Networks (scaling neural networks on graphs)
• Deep learning (scaling deep learning models)