BACKGROUND: Identifying different cell types is a prerequisite step in the analysis of single-cell RNA sequencing (scRNA-seq) data, with clustering being a common technique utilized for this purpose. However, high dropout rates inherent in scRNA-seq data and complex intercellular relationships become main challenges in scRNA-seq data analysis. RESULTS: To address these issues, we proposed a novel model based on zero-inflated negative binomial (ZINB) distribution and graph attention network for scRNA-seq data clustering (scZGA). scZGA consists of three key modules. The first module captures the global probabilistic structure using a ZINB model. The second module constructs the graph with Pearson's correlation coefficient, and employs a graph autoencoder with residual connection to learn important neighbor relationships while preserving topological structure information simultaneously. The final module conducts deep clustering through a self-optimizing embedding algorithm. CONCLUSIONS: With these improvements, clustering results show that scZGA consistently achieves higher scores across six scRNA-seq datasets by using evaluation metrics such as normalized mutual information and adjusted rand index.

Authoring graph visualization requires advanced programming skills, expert domain knowledge, and significant workload. Existing authoring tools either support limited templates of graph visualization, or suffer from a high learning cost. We analyze the design requirements on a tool for graph visualizations, and contribute Nuwa, a user-friendly declarative authoring tool for the interactive specification of graph visualizations in terms of data, entity, change, and encoding. Our implementation empowers users to conveniently create, compare, and modulate comprehensive graph visualizations with a wide range of styles. We showcase various examples to verify the expressiveness of Nuwa. Via an expert interview and the analysis on cognitive dimensions we evaluate the usability of Nuwa.

Data imputation is an essential pre-processing task for data governance, aimed at filling in incomplete data. However, conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data, and they fail to achieve the best balance between accuracy and efficiency. In this paper, we present a novel visual analysis approach for data imputation. We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables. Then, we perform the initial imputation of incomplete data using correlated data entries from other tables. Additionally, we develop a visual analysis system to refine data imputation candidates. Our interactive system combines the multi-party data imputation approach with expert knowledge, allowing for a better understanding of the relational structure of the data. This significantly enhances the accuracy and efficiency of data imputation, thereby enhancing the quality of data governance and the intrinsic value of data assets. Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using their domain knowledge.

With the acceleration of economic globalization, a large amount of research studies have been conducted for the exploration of industrial economics. In the visualization community, common visualization tools present potential features of industrial economics. They hardly meet the various and complex user requirements for insightful analysis and decision-making. In this article, we design VIEA, a web-based visualization system that integrates a rich set of views and tailored interactions, enabling users to easily perceive economic features, such as geographical distributions, trade relationships, and pattern comparisons. Case studies and user studies based on real-world datasets have been conducted to demonstrate the effectiveness of our system in the exploration of industrial economics.

Node-link diagrams are the most popular form for graph visualization. Yet, salient information of a node-link diagram cannot be fully depicted by solely presenting the visualization. We propose to augment node-link diagrams by creating textual descriptions for interested information. We conduct an expert review and a user interview to identify six requirements of generated interpretations, including three requirements for connection extraction and three requirements for visual expression. Our solution, GraphDescriptor, generates textual descriptions with two stages: feature extraction and description generation. The first one identifies and extracts features of node-link diagrams, like node connections, visual designs, and types of graph layouts. The second stage creates a group of hierarchical sentences based on a pre-defined schema. To the best of our knowledge, our approach is the first attempt to generate textual descriptions automatically. Three use cases and the in-lab user study confirm the superiority of our approach.

In the medical imaging field, Computer-Aided Detection (CADe) has greatly benefited from the recent development of Graph Convolutional Networks (GCNs). GCN-based predictive models require building a population graph to detect the disease states of each subject, based on imaging and non-imaging data. Until now, all existing population-level methods are homogeneous, failing to consider sex differences. To address this issue, we present a heterogeneous population graph convolutional network with hierarchical attention mechanisms, including intra-level and inter-level attention. Specifically, the intra-level attention layer is aimed at learning differences and similarities between the sexes, while the inter-level attention layer is responsible for information integration by assigning weights to different features. The objective is to obtain node embeddings describing individual characteristics completely and provide discriminative inputs to classifiers. Compared to benchmark models, our proposal achieves satisfying prediction results on three datasets, illustrating the framework’s ability to extract predictive attributes from medical multimodal data.

We design and evaluate a novel layout fine-tuning technique for node-link diagrams that facilitates exemplar-based adjustment of a group of substructures in batching mode. The key idea is to transfer user modifications on a local substructure to other substructures in the entire graph that are topologically similar to the exemplar. We first precompute a canonical representation for each substructure with node embedding techniques and then use it for on-the-fly substructure retrieval. We design and develop a light-weight interactive system to enable intuitive adjustment, modification transfer, and visual graph exploration. We also report some results of quantitative comparisons, three case studies, and a within-participant user study.

Graph visualization plays an important role in several fields, such as social media networks, protein–protein interaction networks, and traffic networks. A number of visualization design tools and programming toolkits have been widely used in graph-related applications. However, a key challenge remains in the high-efficiency visualization of large-scale graph data. In this study, we present NetV.js, an open-source and WebGL-based JavaScript library that supports the fast visualization of large-scale graph data (up to 50 thousand nodes and 1 million edges) at an interactive frame rate with a commodity computer. Experimental results demonstrate that our library outperforms existing toolkits (Sigma.js, D3.js, Cytoscape.js, and Stardust.js) in terms of performance.

Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills. Although existing libraries and tools reduce the difficulty of generating graph visualization, there are still many challenges. We work closely with developers and formulate several design goals, then design and implement G6, a web-based library for graph visualization. It combines template-based configuration for high usability and flexible customization for high expressiveness. To enhance development efficiency, G6 proposes a range of optimizations, including state management and interaction modes. We demonstrate its capabilities through an extensive gallery, a quantitative performance evaluation, and an expert interview. G6 was first released in 2017 and has been iterated for 317 versions. It has served as a web-based library for thousands of applications and received 8312 stars on GitHub.

Accurately forecasting the future movements of surrounding vehicles is essential for safe and efficient operations of autonomous driving cars. This task is difficult because a vehicle's moving trajectory is greatly determined by its driver's intention, which is often hard to estimate. By leveraging attention mechanisms along with long short-term memory (LSTM) networks, this work learns the relation between a driver's intention and the vehicle's changing positions relative to road infrastructures, and uses it to guide the prediction. Different from other state-of-the-art solutions, our work treats the on-road lanes as non-Euclidean structures, unfolds the vehicle's moving history to form a spatio-temporal graph, and uses methods from Graph Neural Networks to solve the problem. Not only is our approach a pioneering attempt in using non-Euclidean methods to process static environmental features around a predicted object, our model also outperforms other state-of-the-art models in several metrics. The practicability and interpretability analysis of the model shows great potential for large-scale deployment in various autonomous driving systems in addition to our own.

Path planning is a key component in motion planning for autonomous vehicles. A path specifies the geometrical shape that the vehicle will travel, thus, it is critical to safe and comfortable vehicle motions. For urban driving scenarios, autonomous vehicles need the ability to navigate in cluttered environment, e.g., roads partially blocked by a number of vehicles/obstacles on the sides. How to generate a kinematically feasible and smooth path, that can avoid collision in complex environment, makes path planning a challenging problem. In this paper, we present a novel quadratic programming approach that generates optimal paths with resolution-complete collision avoidance capability.

A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire structure. In this study, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verify the effectiveness of our system.