Data augmentation for graph data involves creating modified versions of existing graphs to expand training datasets and improve machine learning model performance. Unlike tabular or image data, graphs have complex structures (nodes, edges, and relationships), which require specialized techniques. The goal is to generate plausible variations of the original data while preserving essential structural and semantic properties. Common methods include modifying node features, altering edge connections, or sampling subgraphs, all while ensuring the augmented data remains meaningful for the task (e.g., node classification or link prediction).
One approach is edge perturbation, which adds or removes edges to simulate different connectivity patterns. For example, in a social network graph, randomly deleting 5% of edges could mimic missing connections, while adding synthetic edges between nodes with similar features might represent undiscovered relationships. Another technique is node feature masking, where a subset of node attributes (like user age in a recommendation system) is temporarily hidden during training. This forces models to rely on other features or graph structure, improving robustness. Subgraph sampling, such as extracting random walks or ego networks, is also widely used. For instance, sampling a 2-hop neighborhood around a protein node in a molecular graph helps focus on local interactions without processing the entire graph.
However, graph augmentation requires careful design. Unlike images, graphs have interdependent elements—changing a node or edge can cascade through the structure. For example, removing a critical bridge node might disconnect the graph, breaking meaningful relationships. Techniques like adaptive edge addition (only connecting nodes with high feature similarity) or structure-aware node dropping (preserving graph connectivity) mitigate these risks. Tools like PyTorch Geometric or DGL often include utilities for these operations. When implementing augmentation, developers should validate that key properties (e.g., degree distribution or community structure) remain intact using metrics like clustering coefficient or diameter. Balancing randomness and domain logic (e.g., not adding invalid chemical bonds in molecular graphs) is essential for effective augmentation.