Distributed Systems

Distributed systems encompass techniques, frameworks, and architectures for processing computation and data across multiple machines or processors. In the context of misinformation detection, distributed systems enable scaling detection algorithms to large social networks, massive news corpora, and real-time fact-checking pipelines.

Key challenges in distributed systems include: partitioning (how to divide computation/data across machines), communication (how to coordinate and synchronize across machines), fault tolerance (recovery from node failures), and consistency (ensuring correct results despite asynchronous execution).

Key papers

• The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey — comprehensive survey of distributed systems for graph neural networks, covering partitioning, sampling, communication, synchronization, and parallelism techniques

Related topics

• Scalability (making systems handle larger problems)
• Graph Processing (distributed algorithms on graph-structured data)
• Graph Neural Networks (neural network models on graphs, often trained on distributed systems)