GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks (2103.08826v1)

Abstract: Node classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. However, existing GNNs address the problem where node samples for different classes are balanced; while for many real-world scenarios, some classes may have much fewer instances than others. Directly training a GNN classifier in this case would under-represent samples from those minority classes and result in sub-optimal performance. Therefore, it is very important to develop GNNs for imbalanced node classification. However, the work on this is rather limited. Hence, we seek to extend previous imbalanced learning techniques for i.i.d data to the imbalanced node classification task to facilitate GNN classifiers. In particular, we choose to adopt synthetic minority over-sampling algorithms, as they are found to be the most effective and stable. This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs. Moreover, node attributes are high-dimensional. Directly over-sampling in the original input domain could generates out-of-domain samples, which may impair the accuracy of the classifier. We propose a novel framework, GraphSMOTE, in which an embedding space is constructed to encode the similarity among the nodes. New samples are synthesize in this space to assure genuineness. In addition, an edge generator is trained simultaneously to model the relation information, and provide it for those new samples. This framework is general and can be easily extended into different variations. The proposed framework is evaluated using three different datasets, and it outperforms all baselines with a large margin.

• The paper presents a novel extension of SMOTE for graphs by generating synthetic nodes and edges to address imbalanced classification.
• It integrates a GNN-based feature extractor with an edge generator to preserve node topology and enhance classifier performance.
• Empirical results on datasets like Cora and Twitter show improved F-measure and AUC-ROC, underscoring robust performance across classes.

Overview of "GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks"

The paper "GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks" addresses the imbalanced classification problem inherent to graph-structured data. This issue occurs when certain node classes have significantly fewer samples than others, potentially leading to reduced classification performance by Graph Neural Networks (GNNs). The paper presents a novel framework, GraphSMOTE, tailored to generate synthetic nodes and predict their associations to mitigate the class imbalance problem effectively.

Key Contributions

1. Extension of Synthetic Minority Over-sampling Techniques (SMOTE): The authors adapt the synthetic minority over-sampling concept for node classification on graphs. Traditional SMOTE methods are ill-equipped to handle graph data due to the necessity of representing relationships among nodes. GraphSMOTE synthesizes new samples within an embedding space, leveraging a GNN-based feature extractor to maintain node similarities and generate natural, in-domain synthetic nodes.
2. Integration of Edge Information: A significant challenge in extending SMOTE to graphs is the generation of relation information. GraphSMOTE employs an edge generator to predict and incorporate links for synthetic nodes, which is crucial for GNN classifiers. This edge predictor is trained to reconstruct the genuine edge distribution, thereby facilitating reliable edge generation.
3. Comprehensive Framework: GraphSMOTE integrates a feature extractor, synthetic node generator, edge predictor, and GNN classifier into a cohesive model. The feature extractor, based on GraphSage, learns embeddings that preserve node properties and topology, while the synthetic node generator interpolates in this embedding space. Once synthetic nodes are formed, the edge generator augments the graph with predicted relationships, and the GNN classifier operates on the augmented graph to enhance classification efficacy.

Numerical Results

The implementation of GraphSMOTE across various datasets, including Cora, BlogCatalog, and Twitter, demonstrates its superior performance over baselines. Specifically, it shows marked improvements over conventional over-sampling, re-weighting, and direct application of SMOTE in graphs. The empirical analysis highlights GraphSMOTE’s capacity to yield balanced class performance, evidenced by improved F-measure and AUC-ROC scores, signifying its strength in both minority and majority class prediction.

Implications and Future Directions

GraphSMOTE's framework has substantial practical implications, notably in fields where graph-structured data with imbalanced classes is prevalent, such as social network analysis and fraud detection. The ability to synthesize nodes and edges that realistically capture the characteristics and relationships of underrepresented classes enhances utility in detecting rare but significant instances, such as fraudulent accounts in a network.

Theoretically, GraphSMOTE opens avenues for refining data-augmentation strategies within graph embeddings, potentially impacting network science, machine learning, and artificial intelligence. Future work could involve extending the framework to other graph-based tasks like link prediction and unsupervised representation learning, or integrating domain-specific constraints into the synthetic node generation process. Additionally, adapting the framework for dynamic graphs or multi-relational and heterogeneous graphs could broaden its applicability and enhance its relevance in complex network analysis.

Overall, the paper presents a significant contribution to handling imbalanced data within graph structures, underscoring the seamless integration of node and edge synthesis to bolster the robustness of GNN-based node classification.