VAC: Visual Analysis of Combined Causality in Event Sequences.

Identifying causality behind complex systems plays a significant role in different domains, such as decision-making, policy implementations, and management recommendations. However, existing causality studies on temporal event sequence data mainly focus on individual causal discovery, which is incapable of capturing combined causality. To address the gap in combined causality discovery on temporal event sequence data, eliminating and recruiting principles are defined to balance the effectiveness and controllability of cause combinations.

Generative and contrastive graph representation learning with message passing.

Self-supervised graph representation learning (SSGRL) has emerged as a promising approach for graph embeddings because it does not rely on manual labels. SSGRL methods are generally divided into generative and contrastive approaches. Generative methods often suffer from poor graph quality, while contrastive methods, which compare augmented views, are more resistant to noise.

Motion-Aware Memory Network for Fast Video Salient Object Detection.

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, these methods still suffer from high computational costs or poor quality of the generated saliency maps. To address this, we design a space-time memory (STM)-based network that employs a standard encoder-decoder architecture.

Multilevel Visual Analysis of Aggregate Geo-Networks.

Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well.

Rigel: Transforming Tabular Data by Declarative Mapping.

We present Rigel, an interactive system for rapid transformation of tabular data. Rigel implements a new declarative mapping approach that formulates the data transformation procedure as direct mappings from data to the row, column, and cell channels of the target table. To construct such mappings, Rigel allows users to directly drag data attributes from input data to these three channels and indirectly drag or type data values in a spreadsheet, and possible mappings that do not contradict these interactions are recommended to achieve efficient and straightforward data transformation.

MUSE: Visual Analysis of Musical Semantic Sequence.

Visualization has the capacity of converting auditory perceptions of music into visual perceptions, which consequently opens the door to music visualization (e.g., exploring group style transitions and analyzing performance details).

DT2VIS: A Focus+Context Answer Generation System to Facilitate Visual Exploration of Tabular Data.

The visual analysis dialog system utilizing natural language interface is emerging as a promising data analysis tool. However, previous work mostly focused on accurately understanding the query intention of a user but not on generating answers and inducing explorations. A focus+context answer generation approach, which allows users to obtain insight and contextual information simultaneously, is proposed in this work to address the incomplete user query (i.

StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams.

Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC.

Interplay between Public Attention and Public Emotion toward Multiple Social Issues on Twitter.

This study aims to elucidate the intricate interplay between public attention and public emotion toward multiple social issues. A theoretical framework is developed based on three perspectives including endogenous affect hypothesis, affect transfer hypothesis, and affective intelligence theory. Large-scale longitudinal data with 265 million tweets on five social issues are analyzed using a time series analytical approach.

Embedding Spatio-Temporal Information into Maps by Route-Zooming.

Analysis and exploration of spatio-temporal data such as traffic flow and vehicle trajectories have become important in urban planning and management. In this paper, we present a novel visualization technique called route-zooming that can embed spatio-temporal information into a map seamlessly for occlusion-free visualization of both spatial and temporal data. The proposed technique can broaden a selected route in a map by deforming the overall road network.

EvoRiver: Visual Analysis of Topic Coopetition on Social Media.

Cooperation and competition (jointly called "coopetition") are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects.