Approximate Network Motif Mining Via Graph Learning

TL;DR

This paper introduces MotiFiesta, a differentiable machine learning approach for approximate network motif mining, enabling efficient motif discovery and feature extraction in graph datasets.

Contribution

It formulates motif mining as a node labeling problem, develops benchmark datasets, and proposes MotiFiesta, the first fully differentiable method for this task.

Findings

MotiFiesta achieves promising results on challenging benchmarks.

The approach enables simultaneous motif discovery and graph classification.

Benchmark datasets facilitate evaluation of motif mining models.

Abstract

Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets. However, the high computational complexity of identifying motif sets in arbitrary datasets (motif mining) has limited their use in many real-world datasets. By automatically leveraging statistical properties of datasets, machine learning approaches have shown promise in several tasks with combinatorial complexity and are therefore a promising candidate for network motif mining. In this work we seek to facilitate the development of machine learning approaches aimed at motif mining. We propose a formulation of the motif mining problem as a node labelling task. In addition, we build benchmark datasets and evaluation metrics which test the ability of models to capture different aspects of motif discovery such as motif number, size, topology, and scarcity. Next, we propose MotiFiesta, a first attempt at solving this problem in a fully differentiable manner with promising results on challenging baselines. Finally, we demonstrate through MotiFiesta that this learning setting can be applied simultaneously to general-purpose data mining and interpretable feature extraction for graph classification tasks.