Scalable Graph Neural Networks via Bidirectional Propagation (2010.15421v3)

Abstract: Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use "graph sampling" or "layer-wise sampling" techniques to reduce training time. However, these methods still suffer from degrading performance and scalability problems when applying to graphs with billions of edges. This paper presents GBP, a scalable GNN that utilizes a localized bidirectional propagation process from both the feature vectors and the training/testing nodes. Theoretical analysis shows that GBP is the first method that achieves sub-linear time complexity for both the precomputation and the training phases. An extensive empirical study demonstrates that GBP achieves state-of-the-art performance with significantly less training/testing time. Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1.8 billion edges in less than half an hour on a single machine. The codes of GBP can be found at https://github.com/chennnM/GBP .

• The paper presents GBP, a scalable method for graph neural networks that overcomes the limitations of traditional sampling approaches.
• Its bidirectional propagation technique achieves sub-linear time complexity in both precomputation and training, significantly reducing computational and memory costs.
• Empirical evaluations on graphs with billions of edges validate GBP's state-of-the-art performance and practical viability for large-scale applications.

Overview of "Scalable Graph Neural Networks via Bidirectional Propagation"

The paper entitled "Scalable Graph Neural Networks via Bidirectional Propagation" presents an innovative approach to enhance the scalability of Graph Neural Networks (GNNs) when dealing with exceptionally large graphs. As GNNs have gained prominence for their ability to perform learning tasks on non-Euclidean data, their applicability is constrained by the computational complexities associated with large datasets. This paper introduces Graph neural network with Bidirectional Propagation (GBP), designed to overcome these computational barriers.

Key Contributions

The primary contribution of this work is the development of the GBP, a scalable GNN methodology. GBP differentiates itself from existing scalability solutions that predominantly rely on graph or layer-wise sampling to manage computational load. These methods, while effective in reducing training time, falter under the constraints of graphs containing billions of edges. GBP addresses these limitations through a process termed 'bidirectional propagation,' which involves localized propagation from both feature vectors and node sets used in training and testing phases.

Theoretical Foundations and Results

A significant theoretical advancement introduced in this paper is the sub-linear time complexity GBP achieves during both precomputation and training phases. Theoretical analyses corroborate that GBP stands as the first GNN method to achieve this level of computational efficiency, which is crucial for handling extensive graph structures. The bidirectional propagation strategy exploits locality to significantly reduce both computational complexity and memory usage.

Empirical Performance

An extensive empirical paper underscores GBP’s potential by demonstrating state-of-the-art performance with considerably reduced training and inference times across several datasets. Remarkably, GBP showcases superior capabilities on a graph exceeding 60 million nodes and 1.8 billion edges, completing the computational tasks in under half an hour using a single machine. This demonstrates the practical viability of GBP for extremely large-scale graph analysis, markedly improving accessibility for applications requiring rapid computation over large datasets.

Implications of the Research

From a practical standpoint, GBP facilitates the deployment of GNN models in domains where real-time or near-real-time processing of large-scale graph data is imperative. These include areas like social network analysis, where the expanse and dynamism of data necessitate robust and scalable analytical frameworks. Theoretically, the sub-linear time complexities achieved suggest promising directions for future algorithmic developments in graph-based learning paradigms.

Future Directions

While the research sets a foundational basis, there are avenues for further exploration. Future research could aim to extend GBP’s applicability to heterogeneous networks, wherein nodes and edges accompany varied types and interactions. Additionally, enhancing the framework’s adaptability to dynamically evolving graphs could further bolster its applicability. Another potential direction is the refinement of bias and variance trade-offs in approximation methods for different graph characteristics.

In conclusion, this paper makes a substantial contribution to the field of GNNs by addressing the crucial need for scalability on large-scale graphs. Through its innovative bidirectional propagation mechanism, GBP presents a practical and theoretically sound solution, advancing both the academic understanding and practical applications of GNNs.