Data-related challenges
Graph data presents unique challenges due to its inherent complexity and diverse nature. In this section, we explore three key data-related challenges that significantly impact the development and application of graph learning algorithms.
Heterogeneity in graph structures
Graphs in different domains can have vastly different structural properties:
- Node and edge types: Many real-world graphs are heterogeneous, containing multiple types of nodes and edges. For instance, in an academic network, nodes could represent authors, papers, and conferences, while edges could represent authorship, citations, or attendance.
- Attribute diversity: Nodes and edges may have associated attributes of various types (numerical, categorical, textual), adding another layer of complexity to the learning process.
- Structural variations: Graphs can exhibit different global structures (for example, scale-free, small-world, random) and local patterns (for example, communities...
