Billion Scale Graph

Billion-scale graph processing focuses on developing efficient algorithms and systems for analyzing and learning from extremely large graphs, exceeding billions of nodes and edges. Current research emphasizes distributed computing frameworks, optimized graph partitioning strategies, and novel algorithms like those based on random walks and hypergraph models to overcome the computational and memory limitations of traditional methods. These advancements are crucial for enabling machine learning tasks, such as graph neural network training and graph embedding, on massive real-world datasets arising from social networks, knowledge graphs, and other domains, leading to improved performance and scalability in various applications.