Efficient Graph Distillation for Graph Classification using Computation Tree Patterns

Core Concepts

MIRAGE, a novel graph distillation algorithm, exploits the skewed distribution of computation trees in graphs to condense the training data without compromising model performance. MIRAGE is architecture-agnostic and computationally efficient, outperforming state-of-the-art baselines in accuracy, compression, and distillation speed.

Abstract

The paper introduces MIRAGE, a novel graph distillation algorithm for graph classification tasks. The key insights are:

Graph Neural Networks (GNNs) decompose input graphs into a set of computation trees, and the frequency distribution of these trees often follows a power-law.
MIRAGE exploits this skewed distribution by mining the frequently co-occurring computation trees and using them to train the GNN model. This approach is in contrast to existing graph distillation algorithms that aim to replicate the gradient trajectory of the original training set.
MIRAGE is model-agnostic, as it operates on the computation trees directly, without relying on specific GNN architectures or hyperparameters. This makes it robust to changes in the modeling pipeline.
Extensive experiments on real-world datasets show that MIRAGE outperforms state-of-the-art baselines in terms of prediction accuracy, data compression, and distillation efficiency. On average, MIRAGE achieves 4-5 times higher compression and 150 times faster distillation compared to the baselines.
The authors also provide theoretical analysis and empirical evidence to demonstrate the sufficiency of frequent computation tree patterns in capturing the essential characteristics of the dataset.

Stats

The average number of nodes in the datasets ranges from 13 to 284.
The average number of edges in the datasets ranges from 26 to 715.
The datasets cover various domains, including molecules, proteins, and movies.

Quotes

"GNNs, like other deep learning models, are data and computation hungry. There is a pressing need to scale training of GNNs on large datasets to enable their usage on low-resource environments."
"Existing graph distillation algorithms themselves rely on training with the full dataset, which undermines the very premise of graph distillation."
"The distillation process is specific to the target GNN architecture and hyper-parameters and thus not robust to changes in the modeling pipeline."

Key Insights Distilled From

Mirage

by Mridul Gupta... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2310.09486.pdf

Deeper Inquiries

How can the MIRAGE framework be extended to handle heterophilous datasets where the assumption of skewed computation tree distribution may not hold?

In the case of heterophilous datasets where the assumption of skewed computation tree distribution may not hold, the MIRAGE framework can be extended by incorporating more sophisticated techniques for pattern mining and data compression. One approach could involve adapting the frequent pattern mining algorithm to handle diverse and non-skewed distributions of computation trees. This adaptation may involve using more advanced data mining algorithms that can identify patterns in a more nuanced and flexible manner, allowing for the extraction of relevant information from heterogeneous datasets.
Additionally, the framework could be enhanced to incorporate techniques for outlier detection and handling in the distillation process. Outliers in the dataset can significantly impact the effectiveness of the distillation process, especially in the case of heterophilous datasets. By integrating outlier detection mechanisms and strategies for handling outliers, MIRAGE can improve its robustness and adaptability to diverse dataset characteristics.
Furthermore, the framework could explore the use of ensemble methods or hybrid approaches that combine multiple distillation strategies to capture the diverse patterns present in heterophilous datasets. By leveraging the strengths of different distillation techniques, MIRAGE can enhance its ability to distill information from complex and varied datasets effectively.

How can MIRAGE be adapted to work with other types of graphs, such as temporal or multi-relational graphs, beyond the standard graph classification task?

To adapt MIRAGE to work with other types of graphs, such as temporal or multi-relational graphs, the framework can be extended to incorporate specialized techniques for handling the unique characteristics of these graph structures.
For temporal graphs, MIRAGE can be modified to capture the temporal dynamics and evolving patterns in the data. This adaptation may involve incorporating time-aware features and considering the temporal dependencies between nodes and edges in the distillation process. By integrating temporal information into the computation tree generation and pattern mining steps, MIRAGE can effectively distill information from temporal graphs.
Similarly, for multi-relational graphs, MIRAGE can be enhanced to handle the complex relationships and interactions between different types of entities in the graph. This adaptation may involve developing specialized algorithms for extracting and representing multi-relational patterns in the data. By considering the diverse types of relationships and entities in the graph, MIRAGE can generate more comprehensive and informative distilled datasets for tasks involving multi-relational graphs.
Overall, by customizing the computation tree generation, pattern mining, and distillation processes to suit the specific characteristics of temporal or multi-relational graphs, MIRAGE can be adapted to work effectively with a wide range of graph structures beyond standard graph classification tasks.

What are the potential applications of the MIRAGE framework in domains beyond graph classification, such as graph generation or graph-based reasoning?

The MIRAGE framework has the potential to be applied in various domains beyond graph classification, including graph generation and graph-based reasoning tasks.
In the context of graph generation, MIRAGE can be utilized to distill essential patterns and structures from existing graph datasets and leverage this distilled knowledge to generate new graphs that exhibit similar characteristics. By using the distilled dataset as a basis for generating new graphs, MIRAGE can facilitate the creation of synthetic graphs that capture the underlying patterns and relationships present in the original data.
In graph-based reasoning tasks, MIRAGE can be employed to distill key information from complex graph structures and enhance the efficiency of reasoning algorithms. By compressing the graph data into a more concise and informative representation, MIRAGE can improve the performance of graph-based reasoning models by providing them with a more focused and relevant dataset for inference and decision-making.
Overall, the MIRAGE framework's ability to distill essential information from graph datasets can be leveraged in various applications beyond graph classification, including graph generation and graph-based reasoning, to enhance the efficiency and effectiveness of graph-related tasks in diverse domains.