Rare category detection is an important topic in data mining, which focuses on identifying the very first example from rare categories by requesting only a small number of labels. Most existing techniques for rare category detection are designed for tabular data sets, thus not suitable for graph data. Moreover, the few applicable techniques to graph data require prior information about data sets and will become useless without such information. In this paper, we introduce RCDVis, a visual analysis system designed to support prior-free rare category detection on graph data. RCDVis adopts a community detection-based algorithm RCDGD to identify candidate vertices of rare categories. Furthermore, the algorithm is tightly integrated with an interactive interface which supports the effective exploration of graph data and the labeling of rare categories. This paper (1) introduces the prior-free rare category detection algorithm; (2) describes the visualization and interaction designs of user interface; and (3) presents results from quantitative experiments and example usage scenarios to demonstrate the effectiveness of RCDVis.
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