Interactive Visual Pattern Search on Graph Data via Graph Representation Learning
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 86 references that are in the map. First posted on arXiv 18 Feb 2022.
Builds on
26 of 86 references.
References · 26
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
- 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
- 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
- 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 202
- 2014Propagation Kernelscited by 88
- 2020Deep Graph Matching Consensuscited by 85
- 2020Directed Graph Convolutional Networkcited by 73
14 more
- 2020Neural Subgraph Matchingcited by 47
- 2018REGAL: Representation Learning-based Graph Alignmentcited by 40
- 2020Structured Query-Based Image Retrieval Using Scene Graphscited by 33
- 2019Neural Subgraph Isomorphism Countingcited by 27
- 2020Image-to-Image Retrieval by Learning Similarity between Scene Graphscited by 23
- 2019A Deep Generative Model for Graph Layoutcited by 19
- 2019Deep Graph Similarity Learning: A Surveycited by 19
- 2019DeepDrawing: A Deep Learning Approach to Graph Drawingcited by 15
- 2018Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approachcited by 14
- 2019DDGK: Learning Graph Representations for Deep Divergence Graph Kernelscited by 14
- 2018GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Modelscited by 8
- 2020Integrating Prior Knowledge in Mixed Initiative Social Network Clusteringcited by 6
- 2017What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualizationcited by 5
- 2020A Visual Analytics Framework for Contrastive Network Analysiscited by 3
Built on by
Citing papers per year: 2023: 1.
1 paper.