Summary
In this chapter, we have explored the multifaceted challenges that define the current landscape of graph learning. From fundamental issues of handling large-scale, heterogeneous, and dynamic graph data to intricate problems of designing effective GNN architectures, each challenge presents unique obstacles and opportunities for innovation. We’ve examined the computational hurdles of processing massive graphs, nuanced difficulties in specific tasks such as node classification and link prediction, and the growing demand for interpretable and explainable models.
These challenges are not isolated; they intersect and compound each other, creating a complex ecosystem of problems that researchers and practitioners must navigate. As graph learning continues to evolve and find applications in critical domains such as healthcare, finance, and social sciences, addressing these challenges becomes not just an academic pursuit but a practical necessity.
The future of graph learning...
