2015/05/26 by Robert Pienta, Zhiyuan Lin, Pienta, Robert +14
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Data Visualization and Analytics #FOS: Computer and information sciences #Information Retrieval (cs.IR) #cs.IR
paper · pdf · doi:10.48550/arxiv.1505.06792
openalex publication_date 2015/05/26 · arxiv created 2016/07/22 · arxiv updated 2016/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Visualization is a powerful paradigm for exploratory data analysis. Visualizing large graphs, however, often results in a meaningless hairball. In this paper, we propose a different approach that helps the user adaptively explore large million-node graphs from a local perspective. For nodes that the user investigates, we propose to only show the neighbors with the most subjectively interesting neighborhoods. We contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as well as how well it fits the nodes the user chose to explore. We introduce FACETS, a fast and scalable method for visually exploring large graphs. By implementing our above ideas, it allows users to look into the forest through its trees. Empirical evaluation shows that our method works very well in practice, providing rankings of nodes that match interests of users. Moreover, as it scales linearly, FACETS is suited for the exploration of very large graphs.