Interactive Visual Pattern Search on Graph Data via Graph Representation Learning

TL;DR

This paper introduces GraphQ, a visual analytics system that uses graph neural networks to enable fast, interactive, example-based subgraph pattern search in large graph databases, with a novel node-alignment GNN called NeuroAlign.

Contribution

The paper presents a novel GNN for node-alignment called NeuroAlign and a visual system for interactive subgraph search, improving accuracy and speed over existing methods.

Findings

NeuroAlign improves node-alignment accuracy by 19-29%.

GraphQ achieves up to 100x speedup in subgraph matching.

Qualitative user studies confirm effectiveness in real-world scenarios.

Abstract

Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in graphs is an important approach to understanding their structural properties. We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search in a database containing many individual graphs. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation, and perform subgraph matching in the latent space. Due to the complexity of the problem, it is still difficult to obtain accurate one-to-one node correspondences in the matching results that are crucial for visualization and interpretation. We, therefore, propose a novel GNN for node-alignment called NeuroAlign, to facilitate easy validation and interpretation of the query results. GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints. We demonstrate GraphQ through two example usage scenarios: analyzing reusable subroutines in program workflows and semantic scene graph search in images. Quantitative experiments show that NeuroAlign achieves 19-29% improvement in node-alignment accuracy compared to baseline GNN and provides up to 100x speedup compared to combinatorial algorithms. Our qualitative study with domain experts confirms the effectiveness for both usage scenarios.