RESUMEN
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 understand 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 100× speedup compared to combinatorial algorithms. Our qualitative study with domain experts confirms the effectiveness for both usage scenarios.
RESUMEN
Bipartite graphs model the key relations in many large scale real-world data: customers purchasing items, legislators voting for bills, people's affiliation with different social groups, faults occurring in vehicles, etc. However, it is challenging to visualize large scale bipartite graphs with tens of thousands or even more nodes or edges. In this paper, we propose a novel visual summarization technique for bipartite graphs based on the minimum description length (MDL) principle. The method simultaneously groups the two different set of nodes and constructs aggregated bipartite relations with balanced granularity and precision. It addresses the key trade-off that often occurs for visualizing large scale and noisy data: acquiring a clear and uncluttered overview while maximizing the information content in it. We formulate the visual summarization task as a co-clustering problem and propose an efficient algorithm based on locality sensitive hashing (LSH) that can easily scale to large graphs under reasonable interactive time constraints that previous related methods cannot satisfy. The method leads to the opportunity of introducing a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. In the framework, we also introduce a compact visual design inspired by adjacency list representation of graphs as the building block for a small multiples display to compare the bipartite relations for different subsets of data. We showcase the applicability and effectiveness of our approach by applying it on synthetic data with ground truth and performing case studies on real-world datasets from two application domains including roll-call vote record analysis and vehicle fault pattern analysis. Interviews with experts in the political science community and the automotive industry further highlight the benefits of our approach.
RESUMEN
Pulmonary atresia with intact ventricular septum (PAIVS) is a morphologically heterogeneous lesion and accounts for 1-3% of critically ill infants with congenital heart disease. Numerous surgical approaches have been attempted with varying degrees of success, but the mortality rate is still high in most series. The optimal surgical procedure depends on the size and morphology of the tricuspid valve and right ventricle and the presence or absence of right ventricle-dependent coronary circulation. Therefore, it is pivotal to define the precise morphologic and hemodynamic characteristics, especially coronary artery anatomy. In this report, we describe a full-term female neonate with cyanosis soon after birth. Two-dimensional and color Doppler echocardiography corroborated the diagnosis of PAIVS and showed a small right ventricle. Cardiac catheterization indicated PAIVS and further revealed right ventricle-dependent coronary circulation. A systemic-to-pulmonary artery shunt was constructed with a positive immediate result.