Computational challenges
As graph learning techniques continue to evolve and find applications in increasingly complex domains, they face significant computational hurdles. The sheer scale and intricacy of real-world graphs pose formidable challenges to existing algorithms and computing infrastructures. Here, we delve into three primary computational challenges that researchers and practitioners encounter when working with large-scale graph data: scalability issues, memory constraints, and the need for parallel and distributed computing solutions.
Scalability issues for large graphs
As graph data continues to grow in size and complexity, scalability has become a critical challenge for graph learning algorithms. This issue is particularly evident in scenarios such as social network analysis or web-scale graphs, where billions of nodes and edges are common. Traditional graph algorithms often have time complexities that scale poorly with graph size, making them impractical for large...
