Lifelong Graph Learning (2009.00647v4)

Abstract: Graph neural networks (GNN) are powerful models for many graph-structured tasks. Existing models often assume that the complete structure of the graph is available during training. In practice, however, graph-structured data is usually formed in a streaming fashion so that learning a graph continuously is often necessary. In this paper, we bridge GNN and lifelong learning by converting a continual graph learning problem to a regular graph learning problem so GNN can inherit the lifelong learning techniques developed for convolutional neural networks (CNN). We propose a new topology, the feature graph, which takes features as new nodes and turns nodes into independent graphs. This successfully converts the original problem of node classification to graph classification. In the experiments, we demonstrate the efficiency and effectiveness of feature graph networks (FGN) by continuously learning a sequence of classical graph datasets. We also show that FGN achieves superior performance in two applications, i.e., lifelong human action recognition with wearable devices and feature matching. To the best of our knowledge, FGN is the first method to bridge graph learning and lifelong learning via a novel graph topology. Source code is available at https://github.com/wang-chen/LGL

• The paper introduces Feature Graph Networks (FGNs), a novel method integrating lifelong learning with Graph Neural Networks by treating features as nodes and forming new graphs from individual nodes.
• FGNs transform node classification problems into graph classification problems, leveraging feature cross-correlation to build adjacency matrices that encode feature interactions while maintaining constant computational overhead.
• Empirical validation on classic datasets shows FGN achieves comparable or superior performance and efficiency in data-incremental and class-incremental settings, with applications in action recognition and feature matching.

Lifelong Graph Learning: A Comprehensive Exploration

The paper "Lifelong Graph Learning" presents a novel approach to integrating lifelong learning methodologies with graph neural networks (GNNs), a fusion that addresses the continuous learning requirements of graph-structured data. This work introduces the concept of Feature Graph Networks (FGN), a new graph topology that enables the transformation of a node classification problem into a graph classification problem. The approach involves using features as nodes and forms new graphs from individual nodes, thereby turning the original node classification challenge into a graph classification task.

Key Contributions

1. Integration with Lifelong Learning: The authors effectively bridge GNNs with lifelong learning frameworks by adapting lifelong learning principles from Convolutional Neural Networks (CNNs). This adaptation is crucial given the dynamic nature of graph data, which often arrives in a streaming fashion with evolving structures over time.
2. Feature Graph Conceptualization: By introducing the feature graph topology, the paper redefines graph nodes as graphs themselves, where features take the role of nodes and are organized according to cross-feature correlations. This structure enables the classical node classification task to be approached as graph classification, thus benefiting from robust graph neural network methodologies.
3. Dynamic Feature Adjacency: The feature graph leverages feature cross-correlation to construct adjacency matrices, effectively encoding feature interactions—crucial for capturing the intrinsic structure of graph-based tasks. This dimensionality reduction ensures a constant computational overhead, addressing the scalability issues inherent in traditional GNN frameworks.
4. Empirical Validation: The paper provides extensive experimental validation using classical datasets like Cora, Citeseer, Pubmed, and ogbn-arXiv. FGN demonstrates efficiency in both data-incremental and class-incremental settings, achieving comparable or superior performance to traditional GNN methods. The experiments further extend to practical applications in lifelong human action recognition and feature matching, highlighting FGN's versatility across diverse domains.

Practical and Theoretical Implications

FGN's formulation offers significant practical benefits in applications requiring continuous learning capabilities and adaptability to changes in graph structure. For instance, in social or citation networks, where nodes (users or articles) and their connections continuously evolve, FGNs provide a scalable and efficient means to maintain model performance without retaining all historical data.

From a theoretical perspective, the introduction of feature graphs and their adjacency matrices for encoding interactions opens new avenues for the exploration of GNN architectures. This work prompts further research into the extraction and utilization of feature interactions, potentially spurring developments in more expressive and computationally efficient GNN models.

Future Directions

Future work could focus on optimizing the vectorization of FGN implementations for more efficient computation, extending the framework to handle vector-based edge weights, and exploring additional lifelong learning algorithms beyond the currently employed methods. Additionally, enhancing FGN's integration with other machine learning paradigms could further broaden its applicability across domains with graph-structured data.

In summary, this paper offers a comprehensive exploration of integrating lifelong learning with GNNs, providing robust solutions to the challenges of streaming and evolving graph data. The introduction of FGNs represents a meaningful stride towards scalable, adaptive graph learning models that maintain efficacy in the face of continuous change.