Visual graph exploration can help the users have an intuitive impression about a dataset for the first time. However, it is better to provide some guidance so that the users can quickly locate the "interesting" or "informative" area in the graph. Then they can start the next stage of sense-making. We propose a visual exploration system which provides some frequent sub-graphs extracted from the whole graph, and the relationship between them. Thus, users can raise their study questions more quickly when they first confront a graph dataset. We evaluate our exploration system with two datasets. Specifically, we demonstrate how to raise a question and find the answer with our system, which validates the effectiveness of this project.

Given a graph structure, different layout algorithms (even different settings of the same algorithm) usually result in different arrangements of vertices, and each layout may reflect certain aspects/parts of the graph more accurately than others. Thus, for high-level graph analysis tasks that rely on the overall arrangement of vertices, drawing conclusions only from one layout is risky. To alleviate the risk, we propose an ensemble framework to capture the commonalities and differences among possible layouts, and help users obtain a comprehensive view of the structure patterns. We leverage a set of layouts that represents the distribution of algorithm outputs. Then, visual features are extracted and analyzed based on various measures of visual similarity. Our framework supports users to analyze individual layouts in the context of the distribution, so that users can quickly identify structures of interest and discover patterns more accurately and comprehensively. We demonstrate the effectiveness of our framework by applying it to three datasets.

It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we introduce a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow.