Source-linked AI summary
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
Ke Sun, Zhouchen Lin, Zhanxing Zhu
TL;DR
Graphs with few labeled nodes make GCN learning difficult because shallow architectures inefficiently propagate limited label information. The paper proposes M3S, combining multi-stage self-training with DeepCluster and an aligning mechanism. Across considered graphs and different label rates, M3S outperforms other state-of-the-art methods, while its current design uses one self-supervised approach and depends on balanced labeled data.
Problem
GCNs have limited label propagation and generalization on graphs with few labeled nodes, a weakly supervised setting with little prior work.
Method
M3S combines a Multi-Stage Training Framework based on Self-Training with DeepCluster and an embedding-space aligning mechanism to construct pseudo-labels.
Results
M3S outperforms other state-of-the-art methods across all considered graphs with few labeled nodes and different label rates.
Takeaways & Limitations
M3S is presented as a novel, efficient algorithm and a general framework for leveraging self-supervision with few labeled data.
Takeaways & Limitations
The work uses one self-supervised approach and relies on a categorically balanced labeled set; broader approaches and applications remain for future work.
Abstract
from arXiv · showhide
Graph Convolutional Networks(GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a popular form of self-supervised learning, and design corresponding aligning mechanism on the embedding space to refine the Multi-Stage Training Framework, resulting in M3S Training Algorithm. Finally, extensive experimental results verify the superior performance of our algorithm on graphs with few labeled nodes under different label rates compared with other state-of-the-art approaches.
Introduction
GCNs face limited label propagation and weak generalization on graphs with few labeled nodes. The paper introduces M3S, combining multi-stage training with DeepCluster-based self-supervision to address this setting.
- GCNs naturally integrate graph connectivity and feature attributes, outperforming traditional MLP and graph-embedding approaches.
- Sparse labels expose two challenges: inefficient propagation through shallow GCNs and limited prior work on weakly supervised graph classification.
- The paper frames efficient GCN training and effective use of unlabeled data through self-supervised learning as its two central research issues.
- M3S combines a Multi-Stage Training Framework with DeepCluster and an embedding-space aligning mechanism for self-supervised training.
- GCNs require more layers to maintain performance as label rates decrease, revealing a layer effect on graphs with few labeled nodes.
- M3S provides a general framework for using self-supervision to improve multi-stage training when only few labeled data are available.
Our Approach
M3S addresses inefficient label propagation in GCNs on graphs with few labeled nodes by combining multi-stage self-training with DeepCluster-based embedding-space self-checking. The method iteratively selects confident nodes, filters them using aligned cluster pseudo-labels, and retrains across stages.
- Motivation: GCN smoothing can restrict deep architectures, while fewer labels require more layers to propagate weak label signals effectively.Repeated Laplacian smoothing converges node embeddings toward values proportional to the square root of vertex degree, restricting convolutional-layer enlargement.
- Multi-Stage Training: Multi-Stage Training repeatedly adds confident unlabeled nodes with virtual labels and retrains the model, extending one-shot self-training.The process runs for K stages, updating labeled and unlabeled sets after each selection.
- DeepCluster: DeepCluster groups graph-embedding vectors with k-means, then alternates between pseudo-label assignment and representation learning.For node classification, clustering is applied to GCN embeddings rather than image features.
- DeepCluster: The aligning mechanism maps each unlabeled cluster to the nearest labeled class centroid in embedding space, producing class-consistent pseudo-labels.Cluster centroid vl is aligned with the labeled-class centroid µm having the closest distance.
- M3S Training Algorithm: M3S combines DeepCluster self-checking with multi-stage training to retain confident nodes only when their predicted class matches the aligned pseudo-label.This filtering is intended to select more precise virtual labels efficiently.
- M3S Training Algorithm: A larger number of DeepCluster clusters helps avoid per-class imbalance and improves the precision and flexibility of embedding-distance self-checking.The paper identifies categorically balanced labeled data as important for low-label-rate graphs and notes that DeepCluster can otherwise fall into trivial solutions.
Experiments
Experiments evaluate GCNs, self-training variants, Multi-Stage Training, and M3S across low-label citation-network settings. Results show layer requirements vary with label rate, Multi-Stage Training helps especially with fewer labels, and M3S consistently performs best across datasets and label rates.
- Experimental Setup: Experiments use CiteSeer, Cora, and PubMed, comparing M3S and Multi-Stage Training with Label Propagation, GCNs, Self-Training, Co-Training, Union, and Intersection.All reported results are mean accuracy over 10 runs.
- Layer Effect: Lower label rates require more graph convolutional layers for the best GCN performance.The best-performing layer count descends as the label rate increases.
- Multi-Stage Training: Self-training methods outperform original GCNs by a large margin, especially when label rates are low.The improvement diminishes as the label rate increases.
- Multi-Stage Training: Multi-Stage Training is superior to traditional Self-Training particularly with fewer labeled nodes, and additional stages tend to bring more improvement.The discrepancy between the two methods narrows as the label rate increases.
- M3S Results: M3S consistently outperforms other state-of-the-art approaches across all label rates and datasets.The method combines Multi-Stage Training with DeepCluster’s self-checking mechanism, with larger improvements at lower label rates.
- Cluster Sensitivity: Increasing DeepCluster cluster counts tends to balance categories until the count is sufficiently large, which facilitates M3S performance.The max-min ratio measures class-balance differences, with lower values indicating greater balance; reported values are means over 10 runs.
Discussions
The discussion identifies the use of only one self-supervised approach as a limitation while motivating broader exploration of self-supervised mechanisms and applications.
- Limitations and Future Work: The study uses only one self-supervised approach for graph learning, leaving alternative aligning mechanisms and self-supervised methods for future exploration.It also identifies extension to image and sentence classification as requiring further work.
Conclusion
The paper identifies a layer effect in GCNs under low label rates and proposes M3S, which combines multi-stage training with self-supervision. M3S outperforms other state-of-the-art methods across considered graphs and label rates.
- With lower label rates, GCNs benefit from more layers to facilitate broader label-information propagation.
- M3S enlarges the labeled training set with confident virtual labels and refines this process using DeepCluster-based embedding alignment.
- M3S outperforms other state-of-the-art methods across all considered graphs with different label rates.