Task-specific challenges
While graph learning algorithms face general challenges, certain tasks present unique difficulties that require specialized approaches. In this section, we consider four common task-specific challenges in graph learning, each with its own set of complexities and proposed solutions.
Node classification in imbalanced graphs
Node classification in real-world graphs often suffers from class imbalance, where some classes are significantly underrepresented. This issue is prevalent in scenarios such as fraud detection in financial transaction networks, where fraudulent transactions are typically rare compared to legitimate ones. The imbalance can lead to biased models that perform poorly on minority classes.
Some approaches to mitigate this include the following:
- Re-sampling techniques, such as over-sampling minority classes or under-sampling majority classes, can be adapted for graph data. For instance, GraphSMOTE extends the Synthetic Minority Over...
